
























United States 
Environmental Protection 
Agency 


f/EPA 

Aquatic Stressors 

framework and implementation 
plan for effects researc 


Linj 





























EPA 600/R-02/074 
September 2002 


Aquatic Stressors 

framework and implementation plan for effects research 


U.S. Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Research Triangle Park, NC 27711 



Recycled/Recyclable 
Printed with vegetable-based ink on 
paper that contains a minimum of 
50% post-consumer fiber content 
processed chlorine free. 


Foreword 


The National Health and Environmental Effects Research Laboratory (NHEERL), as part of the 
Environmental Protection Agency’s (EPA’s) Office of Research and Development (ORD), is 
responsible for conducting research on the effects of anthropogenic stresses on human and 
ecosystem health. This research is intended to address key Agency problems in a timely and 
responsive manner. To meet this responsibility, NHEERL is developing research i 
implementation plans to achieve the following objectives: CyTI 

I ^ 

• Optimize responsiveness of research activities to Agency needs, ^ j a 

• Sharpen the focus of research programs where needed, ^ 0 

• Provide a forum for engagement of scientific staff on issues and approaches, I 

• Focus on multi-year planning explicitly linked to Agency performance goals, and 


• Provide a mechanism for prioritizing research. 

This approach builds on the ORD planning process that identifies and prioritizes research topics. 
Current areas for research include particulate matter, air toxics, drinking water, aquatic stressors, 
support to the Food Quality Protection Act, safe communities and ecosystems, the Environmental 
Monitoring and Assessment Program, ecological risk assessment, human health risk assessment, 
and endocrine disruptors. 

This document identifies the scientific problems and the research that will be conducted 
concerning aquatic stressors. The ultimate goal of this research is to develop scientifically valid 
approaches for protecting the ecological integrity of aquatic ecosystems from multiple aquatic 
stressors, in support of EPA’s goal to provide clean and safe water. The framework section was 
developed by a steering committee composed of representatives from NHEERL Divisions, other 
ORD Laboratories and Centers, EPA’s Office of Water, and EPA’s Regional Offices. 
Implementation plans for research on habitat alteration, nutrients, suspended and bedded 
sediments, toxic chemicals, and diagnostics were developed by work groups in the Ecology 
Divisions. The document is intended to reflect research that will be conducted over the next 
several years. As progress is made in achieving these goals, this document will be updated to 
address new and remaining water quality challenges. 


Lawrence W. Reiter 
Director 

National Health and Environmental Effects Research Laboratory 


LC Control Number 



2003 431682 


















Abstract 


This document describes the framework and research implementation plans for ecological effects 
research on aquatic stressors within the National Health and Environmental Effects Laboratory. 
The context for the research identified within the framework is the common management goal of 
protecting aquatic systems to prevent degradation of habitat, loss of ecosystem function, and 
reduced biodiversity. Five main research products for meeting this goal are identified in the 
framework: 1) methods to predict biological effects of habitat alteration; 2) population, 
community, and ecosystem stressor-response models; 3) diagnostic tools to determine 
impairment or causes of impairment to aquatic systems; 4) classification approaches to aid in the 
prediction and management of problems; and 5) methods and models to support development of 
ecological criteria. The research implementation plans herein focus on the effects of four aquatic 
stressors, including habitat alteration, nutrients, suspended and bedded sediments, and toxic 
chemicals. This approach is consistent with recent scientific consensus, recognizing that these 
stressors have the greatest potential for causing adverse effects to aquatic ecosystems. In 
addition, the document outlines research that will develop diagnostic tools for a decision support 
system for resource managers. The major goals, the critical path for research, specific research 
projects, and a gap analysis are provided for each of the five research implementation plans along 
with the time table of research products that will support EPA’s Goal 2 (Clean and Safe Water) 
research under the Government Performance and Results Act. 


Ill 


Table of Contents 


Foreword . ii 

Abstract .iii 

Acknowledgments.viii 

Glossary.xi 

Executive Summary.xiv 

Section 1. 

Introduction .1 

Purpose and Scope.1 

Programmatic Needs .2 

References.2 

Section 2. 

Research Approach .3 

Context for Research.3 

Research Process.3 

Decision Support System.5 

Section 3. 

Research Products and Implementation Plans.8 

Section 4. 

Implementation Plan for Habitat Alteration Research.10 

Problem .10 

Goals .12 

Critical Path.14 

Research Projects.17 

Gap Analysis .37 

References .38 

Section 5. 

Implementation Plan for Nutrients Research.40 

Problem .40 

Goals ..42 

Critical Path.44 

Research Projects.49 

Gap Analysis .62 

References .62 


IV 






























Table of Contents (continued) 


Section 6. 

Implementation Plan for Suspended and Bedded Sediment Research.66 

Problem .66 

Goals .67 

Critical Path. 68 

Gap Analysis .75 

References.75 

Section 7. 

Implementation Plan for Toxic Chemicals Research.77 

Problem .77 

Goals .78 

Critical Path.79 

Research Projects.95 

Gap Analysis .125 

References.130 

Section 8. 

Implementation Plan for Diagnostics Research.138 

Problem .138 

Goals .139 

Critical Path.141 

Research Projects.146 

Gap Analysis .165 

References.169 

Appendix 1.174 

Appendix 2.179 


V 
























Figures 


Figure 1. Research process and products for meeting the goal of effective management and 


protection of aquatic resources.4 

Figure 2. Manager’s decision support system to protect and restore aquatic resources using 

ORD’s research products (SI box is modified from EPA 2000b).6 


Figure 3. 


Critical path for habitat alteration research (APGs) refer to those listed and described 
in the Goals subsection).15 


Figure 4. 


Components of coastal vegetated habitat with possible pathways for direct and 
indirect effects of habitat alteration on fish, shellfish, and wildlife.21 


Figure 5. Critical path for research on the development of nutrient response relationships for 


coastal receiving waters.45 

Figure 6. Conceptual diagram of the feedbacks among data mining, model development, field 
monitoring, and experimental hypothesis validation.55 

Figure 7. Critical path for suspended and bedded sediments research.69 

Figure 8. Ecological risk assessment framework (modified from EPA 1992).80 


Figure 9. 


Critical path for developing site-specific methodologies for establishing the risks of 
toxic chemicals to aquatic life and aquatic dependent wildlife.81 


Figure 10. Simple conceptual model for risk assessments of nonbioaccumulative toxicants. .. 84 

Figure 11. Conceptual model for risk assessments and criteria development involving 

determination of safe loadings of bioaccumulative toxicants to aquatic systems. .. 91 

Figure 12. Critical path (flow of APGs) for diagnostics research.142 

Figure 13. A logic for characterizing the causes of ecological injuries at specific sites. Modified 


from Figure 4-1 in SI document (EPA 2000c) to show potential inputs from aquatic 
stressors diagnostics research.144 

Figure 14. Relationship between current stages of State/Tribal assessment, TMDL and watershed 
restoration planning processes, and proposed combined path.145 

Figure 15. Locations of national water-quality assessment study units.159 


Figure 16. Conceptual model of cause-and-effect relationships in coastal systems, providing a 
framework for a decision support system. See key to model components at base of 
figure. Loading terms include atmospheric component.163 


VI 
















Tables 


Table 1 
Table 2 
Table 3 

Table 4 

Table 5 

Table 6 

Table 7 


. Time line for habitat alteration research.16 

. List of candidate species for study in marine and Great Lakes coastal regions.18 

. Preliminary list of factors influencing response to excess nutrient inputs in coastal 
receiving waters .46 

. Existing or proposed approaches to classification at regional, watershed, water-body, 
and habitat scales .150 

. Examples of methods incorporated in conceptual framework (Figure 16) for decision 
support system.164 

. Single aquatic stressors method development covered by other research areas within the 
Aquatic Stressors Framework.166 

. FTE resource allocation for diagnostics by year (FW = freshwater, SW = saltwater) 
.168 


• • 
Vll 










Acknowledgments 


Authors 

The principal authors of Sections 1-3 (framework for aquatic stressor research) are members of 
the National Health and Environmental Effects Laboratory’s Aquatic Stressors Steering 
Committee, which is composed of representatives from the Office of Research and 
Development’s (ORD) Laboratories and Centers, Office of Water (OW), and Regional offices. 
Principal authors for Sections 4-8 (Research Implementation Plans for Habitat Alteration, 
Nutrients, Suspended and Bedded Sediments, Toxic Chemicals and Diagnostics) are members of 
work groups from NHEERL’s four Ecology Divisions: Atlantic Ecology Division, Narragansett, 
RI; Gulf Ecology Division, Gulf Breeze, FL; Mid-Continent Ecology Division, Duluth, MN; and 
Western Ecology Division, Corvallis, OR (see below). 


NHEERL Aquatic Stressors Steering Committee 

Mid-Continent Ecology Division (MED) 

Bob Spehar (Chair) 

Jack Kelly (Co-chair) 

Atlantic Ecology Division (AED) 

Jonathan Garber/Steve Schimmel 
Walter Berry 

Gulf Ecology Division (GED) 

Bill Walker 
Skeet Lores 

Western Ecology Division (WED) 

Walt Nelson 
Spence Peterson 

Neurotoxicology Division (NTD) 

Bob MacPhail 

Experimental Toxicology Division (ETD) 

Bob Luebke 

NHEERL Assistant Laboratory Director 
Jennifer Orme Zavaleta/Barbara Walton 

National Exposure Research Laboratory/Ecological Exposure Research Division (EERD) 
Susan Cormier 


VllI 


Acknowledgments (continued) 


National Risk Management Research Laboratory/Subsurface Protection and Remediation 
Division (SPRD) 

Steve Schmelling 

National Center for Environmental Assessment/Exposure Analysis and Risk Characterization 
Group (EARCG) 

Sue Norton 

OW/Health and Ecological Criteria Division (HECD) 

Bill Swietlik 

Regions/Region 4 
Joel Hansel 


NHEERL Aquatic Stressors Work Groups 
Habitat Alteration 

Cathy Wigand, Giancarlo Cicchetti (AED) 

Rich Devereux, John Macauley (GED) 

John Brazner, Anett Trebitz (MED) 

Bob Lackey (Chair), Jim Wigington, Jim Power (WED) 

Nutrients 

Jim Latimer (AED) 

Sheet Lores (Chair), Rick Greene, Michael Murrell (GED) 

Jo Thompson, John Morrice, Jack Kelly, Russ Kreis (MED) 

Pete Eldridge, Robbins Church (WED) 

Suspended and Bedded Sediments 
Walter Berry (AED) 

Bill Walker, Mike Lewis (GED) 

Danny Tanner, Mike Sierszen (MED) 

Steve Paulsen (Chair), Phil Kaufinann, Mark Johnson (WED) 

Toxic Chemicals 

Walter Berry, Matt Mitro, Diane Nacci (AED) 

Larry Goodman, Michael Hemmer (GED) 

Russ Erickson (Chair), Bob Spehar, Rick Bennett, Phil Cook (MED) 
Jennifer Orme-Zavaleta (WED) 

Diagnostics 

Skip Nelson, Dan Campbell, Rob Burgess (AED) 


IX 


Acknowledgments (continued) 


Virginia Engle, Jan Kurtz (GED) 

Naomi Detenbeck (Chair), Larry Burkhard, Bill Richardson (MED) 
Jennifer Orme-Zavaleta, Jana Compton (WED) 


Reviewers 

A preliminary draft of this document was reviewed by staff within the ORD Laboratories and 
Centers, OW, and the Regions. A subsequent draft was reviewed in February 2002 by the 
following reviewers outside of EPA: 


Dr. Brian Bledsoe 
Engineering Research Center 
Colorado State University 

Dr. Scott Dyer 

The Procter and Gamble Company 

Dr. Ken Rose 

Coastal Fisheries Institute 

Louisiana State University 

Dr. Patricia Chow-Fraser 
McMaster University 

Dr. Scott Phillips 
US Geological Survey 
Water Resources Division 


Dr. Keith Solomon 
Center for Toxicology 
University of Guelph 

Dr. Jonathan Higgins 
The Nature Conservancy 

Dr. Paul McCormick 
Everglades Program Team 
Department of the Interior 

Dr. A1 Steinman 

Annis Water Resources Institute 

Lake Michigan Center 

Dr. David Rudnick 

South Florida Water Management District 
Everglades Division 


X 


Glossary 


ACE 

Acute-to-Chronic Estimation 

ACWI 

Advisory Committee on Water Information 

AED 

Atlantic Ecology Division 

AhR 

Aryl hydrocarbon Receptor 

APG 

Annual Performance Goal 

APM 

Annual Performance Measure 

AQUIRE 

AQUatic toxicity Information REtrieval 

AVS 

Acid Volatile Sulfide 

BAF 

Bioaccumulative Accumulation Factor 

BASINS 

Better Assessment Science Interacting Point and Nonpoint Sources 

BLM 

Biotic Ligand Model 

BO 

Biological Opinion 

BSAF 

Bioaccumulative Sediment Accumulation Factor 

CADDIS 

Casual Analysis and Diagnosis Decision Information System 

CENR 

Committee on Environment and Natural Resources 

CFR 

Clark Fork River 

CTR 

California Toxics Rule 

CWA 

Clean Water Act 

CWAP 

Clean Water Action Plan 

DEM 

Digital Evaluation Model 

DO 

Dissolved Oxygen 

EC50 

Effective Concentration (50%) 

ECOTOX 

Ecological Toxicity Database 

EDNA 

Elevation Derivatives for National Applications 

ELS 

Early Life Stage 

EMAP 

Environmental Monitoring and Assessment Program 

EPT 

Ephemeroptera, Plecoptera, Trichoptera 

EqP 

Equilibrium Partitioning 

ESA 

Endangered Species Act 

ESG 

Equilibrium-partioning Sediment Guideline 

FGDC 

Federal Geographic Data Committee 

FTE 

Full Time Equivalent 

FWS 

Fish and Wildlife Service 

GAP 

Gap Analysis Program 

GC/MS 

Gas Chromatography/Mass Spectrometry 

GED 

Gulf Ecology Division 

GIS 

Geographic Information System 

GLEI 

Great Lakes Environmental Indicators 

GPRA 

Government Performance and Results Act 

HAB 

Harmful Algal Bloom 

HAPR 

Habitat Alteration Population-Response Model 

HUC 

Hydrologic Unit Code 

IBI 

Indices of Biotic Integrity 


XI 


Glossary (continued) 


ICE 

K. 

LaMP 

LC50 

MAHA 

MED 

NAWQA 

NCEA 

NED-H 

NEP 

NERL 

NHEERL 

NMDS 

NMFS 

NOAA 

NOAEL 

NPDES 

N/P 

NPS 

NRC 

NRDA 

NRMRL 

OEI 

OPP 

OPPTS 

ORD 

OST 

OW 

OWOW 

PAH 

PBTK/TD 

PBT 

PCB 

PCDD 

PCDF 

PHYTOTOX 

REMAP 

RFP 

SAV 

SI 

SOLEC 

SPARROW 

SPRC 


Interspecies Correlation Estimation 
Octonal-water Partition Coefficient 
Lakewide Management Plan 
Lethal Concentration (50%) 

Mid-Atlantic Highlands Assessment 
Mid-continent Ecology Division 
National Water Quality Assessment 
National Center for Environmental Assessment 
National Elevation Dataset (Hydrology) 

National Estuarine Program 
National Exposure Research Laboratory 

National Health and Environmental Effects Research Laboratory 

Nonmetric Dimensional Scaling 

National Marine Fisheries Service 

National Oceanographic and Atmospheric Administration 

No Observed Adverse Effect Level 

National Pollutant Discharge Elimination System 

Nitrogen/Phosphorus 

Nonpoint Source 

National Research Council 

Natural Resources Damage Assessment 

National Risk Management Research Laboratory 

Office of Environmental Information 

Office of Pesticide Programs 

Office of Prevention, Pesticides, and Toxic Substances 
Office of Research and Development 
Office of Science and Technology 
Office of Water 

Office of Wetlands, Oceans, and Watersheds 

Polycyclic Aromatic Hydrocarbon 

Physiologically-Based Toxicokinetic/Toxicodynamic 

Persistent Bioaccumulative Toxicant 

Polychorinated biphenyls 

Polychorinated dibenzo dioxins 

Polychorinated dibenzo furans 

Terrestrial Plant Toxicity Database 

Regional EMAP 

Request for Proposals 

Submerged Aquatic Vegetation 

Stressor Identification 

State of the Lake Ecosystem Conference 

Spatial Referenced Regressions on Watersheds 

Strategic Planning Research Coordination 


XII 


Glossary (continued) 


STAR 

STORE! 

TCDD 

TERRETOX 

TIE 

TMDL 

uses 

uv 

WATERS 

WED 

WQC 

WQS 

WRS 


Science to Achieve Results 
STOrage and RETrieval database 
2,3,7,8-Tetrachlorodibenzo-p-dioxin 
Terrestrial Wildlife Toxicity Database 
Toxicity Identification Evaluation 
Total Maximum Daily Load 
US Geological Survey 
Ultraviolet 

Watershed Assessment Tracking and Environmental Results System 

Western Ecology Division 

Water Quality Criteria 

Water Quality Standards 

Wildlife Research Strategy 


Xlll 


Executive Summary 


This document describes the framework (Sections 1-3) and implementation plans (Sections 4-8) 
for ecological effects research on aquatic stressors within the U.S. Environmental Protection 
Agency’s (EPA) National Health and Environmental Effects Research Laboratory (NHEERL). 
The ultimate goal of this research is to develop scientifically valid approaches for protecting and 
restoring the ecological integrity of aquatic ecosystems from the impacts of multiple aquatic 
stressors. The immediate focus is to develop and improve assessment methodologies, diagnostic 
capabilities, and ecological criteria to guide management options for 1) protection and restoration 
and 2) remediation efforts to meet designated uses. 

The context for this research is the common management goal of protecting aquatic systems to 
prevent degradation of habitat, loss of ecosystem function, and reduced biodiversity. To this end, 
environmental managers must be able to: 1) assess the condition of an aquatic resource and 
determine the degree of impairment, 2) diagnose the causes of impairment, 3) forecast the effects 
of changes in stressor levels, and 4) develop and implement remediation and maintenance 
strategies. Meeting the goals of effective management and protection of aquatic resources 
requires that multiple research elements be in place to provide the needed tools. This document 
provides a means to develop these tools. The research approach presents a generally linear 
sequence, although many research elements will be conducted simultaneously. 

The research herein focuses on the effects of aquatic stressors, including habitat alteration, 
nutrients, suspended and bedded sediments, and toxic chemicals. This approach is consistent 
with recent scientific consensus, recognizing that these stressors have the greatest potential for 
causing adverse effects to aquatic ecosystems. In the context of this effects research, the 
document also provides research that will develop diagnostic tools for a decision support system 
for resource managers. 

The importance of habitat quality and quantity for maintaining species is indisputable, but 
quantifying exactly how species depend on habitats is multi-faceted and complex. Research is 
needed to quantitatively link alterations in key habitats to provide the scientific basis to 
implement regulations and policies to protect fish, shellfish, and wildlife populations, and the 
ecosystems upon which they depend. To quantitatively assess effects over a range of foreseeable 
stressor conditions, stressor-response relationships need to be determined. These relationships 
provide fundamental information that helps to define response thresholds, or other patterns, and 
to improve aquatic life and aquatic dependent wildlife criteria. As stressor-response 
relationships are determined, research can be directed towards developing a "diagnostic tree" 
approach to list, analyze, and characterize the causes of impairment. EPA’s Stressor 
Identification Workgroup has developed such an approach. 

Another research need is to develop ecosystem classification approaches that allow for 
reasonable extrapolations of diagnostic approaches and stressor-response models. Classification 
is valuable for grouping ecosystems according to similar criteria and for spatially classifying 
ecosystems connected via stressor actions. Since little is known about scale relative to ecosystem 
classification, effects research also will provide guidance about the most appropriate scale for 


XIV 


various ecosystem classification approaches, up to and including the watershed scale. At the 
same time, current criteria, methods, and approaches for stressors need to be improved where 
major uncertainties exist, or developed for others where information is scarce. Finally, all 
aquatic stressor research elements from this process need to be combined to develop management 
strategies to protect the ecological integrity of aquatic ecosystems. A series of EPA workshops 
and workgroup meetings has identified five main research products for meeting this goal: 

• Methods to predict biological effects of habitat alteration; 

• Population, community, and ecosystem stressor-response models; 

• Diagnostic tools to determine impairment or causes of impairment to aquatic systems; 

• Classification approaches to aid in the prediction and management of problems; and 

• Methods and models to support development of ecological criteria. 

In some cases, these products are under development, but in others, development has not yet 
begun. Sections 4-8 of this document provide the plans for implementing research in each of 
NHEERL’s priority areas. 

Section 4 focuses on quantifying the life support functions of specific habitat and habitat 
complexes to predict the biological effects of habitat alteration and/or loss on populations of fish, 
shellfish, and wildlife. The main goal of this research is to quantify the role of habitat in 
maintaining healthy aquatic and aquatic-dependent populations by 1) describing the relationships 
between habitat and biota at the appropriate scales to quantify effects on population endpoints 
due to habitat alteration and 2) synthesizing the cumulative support function of individual 
habitats and aquatic ecosystems, and integrating habitat alteration effects with effects from other 
stressors. Necessary elements of this research include providing: suites of species endpoints, 
assessment and measurement endpoints and strategies, habitat alteration-population response 
relationships, classification schemes, and models for extrapolating data across spacial scales. 
Research projects in this plan deal specifically with coastal vegetated habitat; shoreline, lake, and 
estuary scale habitat; and landscape scale habitat. 

Section 5 centers on understanding the responses of receiving waters to excess nutrients. The 
main goal of this research is to define and quantify relationships between nutrient loading and 
ecological responses for different aquatic resource types to develop the basis for deriving 
numeric nutrient criteria. Principal components of this research include providing conceptual 
models for specific assessment endpoints, a state of the science evaluation to develop and 
improve nutrient-load responses, classification schemes, standard measurement endpoints, and 
nutrient load-response models. Research projects in this plan deal primarily with coastal 
receiving waters and the development of nutrient load-response relationships for dissolved 
oxygen, submerged aquatic vegetation, and food web and community composition changes. 

Section 6 deals with the direct and indirect effects of suspended and bedded sediments in aquatic 
ecosystems. The primary goal of this research is to provide and demonstrate the approach for 


XV 


establishing sediment criteria that support aquatic life in streams/rivers, lakes/reservoirs, 
wetlands, and estuaries. NHEERL's effort concerning suspended and bedded sediments has been 
redirected since this section was first written. The majority of the work in this research area will 
now occur under Goal 8 (EMAP) because EMAP design techniques will be applied to develop 
effect thresholds for suspended and bedded sediments in aquatic systems. Some of these 
techniques are described generally in the Critical Path subsection of this section. However, at 
this time, the effort under aquatic stressors will only include a literature review of suspended and 
bedded sediments research. Results from this review will be combined with EMAP approaches 
to synthesize and evaluate the state of the science. Once the review has been completed, data 
gaps will be identified and additional research will be conducted, if warranted. Additional goals 
and research topics are proposed, but will depend on the results of this combined effort. 

Section 7 focuses on developing methods to reduce uncertainty and significantly advance current 
methods to derive criteria for toxic chemicals. The general goal of this research is to develop 
scientifically-defensible methods for better characterizing the risks of toxic chemicals to aquatic 
and aquatic-dependent populations and communities. The key elements for improving aquatic 
risk assessments and criteria for toxic chemicals include providing methods to: improve criteria 
at the individual level based on improved characterization of risks, link individual-level data to 
population endpoints, support risk assessments for chemicals with limited data, and evaluate 
risks on populations at various spatial scales in the context of other stressors. Research projects 
under this plan center around conceptual models that will support the development and 
demonstration of frameworks for better assessing the risks of both non-bioaccumulative and 
bioaccumulative chemicals. 

Section 8 provides an approach for diagnosing the causes of biological impairment linking 
watersheds with receiving water bodies to support the TMDL process and other regulatory 
programs. The primary goals of this research are to provide: a framework for interpreting cause 
and effect relationships, single-stressor diagnostic methods and models to determine the primaiy 
source of biological impairment of aquatic ecosystems, and methods and models to allocate and 
forecast causality among multiple stressors for use in restoration and remediation actions. The 
principal components of this research area align with the primary goals and will provide: the 
scientific foundation and information management scheme for the 303d listing process, and a 
classification fi-amework for surface waters, watersheds, and regions; diagnostic methods to 
distinguish among major classes of single and multiple aquatic stressors; and diagnostic tools for 
forecasting approaches. Specific research projects will be conducted to establish the conceptual 
framework to guide implementation of diagnostics, provide case studies to develop and test 
methods for both single and multiple stressors, and to establish the structure for a decision 
support system. 

Over the next six years (2002-2008) the proposed research, integrated across areas, will provide 
increasingly sophisticated tools to help resources managers assess ecological conditions, 
diagnose impairment and causes of impairment, and forecast the effects of changes in stressor 
levels. As short-tom to inteimediate-term research on aquatic stressors is completed, this 
implementation plan will change, so that new or continued research will provide the tools 
necessary to identify, assess, and manage aquatic stressors and contaminated sediments to meet 
goals under the Government Performance and Results Act. 


XVI 


Further development of this plan will require continuing interaction between the Office of 
Research and Development’s (ORD) Laboratories and Centers, as well as with EPA’s Program 
Offices and Regions, to ensure that the approaches developed are compatible with those for 
exposure and risk characterization. Collaboration with the Office of Water, Regions, States, and 
Tribes will be essential to ensure that this research directly supports regulatory mandates. In 
addition, it will be essential to integrate the research with future grant initiatives (including 
EPA’s Science to Achieve Results [STAR] program) to ensure that ORD-sponsored research 
complements in-house programs. 


xvii 



if t « 

IL f 
■ 





4 





\ 

.. it-. ■ 

' ■ 

• ' .- 

't'lTfr’^■ 

• >"? •! ■' . 


f r»^ i!r**wi» 

1.- ' ’ - ■ ■ 

1 ^ “ ’ > Y%*^ 

}- 'V ■> • 

■ 40^^^’-Ik ' 


’ V#'' ^ " “V* ^ ■ 


I 


> 


it f,%g- 

- 1 Vi 


/ ■ ^r, * 

k«> f*' 


■>i .■• 

£ N- 




•if. 



>;.«‘J'iiifi*' I 


• i»i-- 




;'V j 


v>- 

4 *.... 


f *■ 


. • • ' ■ ’I,* ' 

»’'* <114 f" 

! 4 ■i^ V‘'*v 


». ‘ 


, I#* ^ 


r , ; 


i * 






,rf" 


, * 

‘ 

> , ■* ■' 


f 

t . .- -i' - 


i^.'' 

^rCf ^ 



pi ** 'f ’" 

lNf<^ 

f 




_ V • • 

^sf 


*'u 

¥ > -. ■<.'' 

4^. - 




* 

•Wv 


.li 


,» J 



Section 1. 
Introduction 


Purpose and Scope 

This document describes the framework and implementation plans for ecological effects research 
on aquatic stressors within the National Health and Environmental Effects Research Laboratory 
(NHEERL) beginning in 2002. The ultimate goal of the planned research is to develop 
scientifically valid approaches for protecting the ecological integrity of aquatic ecosystems from 
multiple aquatic stressors. The framework first defines the context and process for conducting 
research to reduce the risks aquatic stressors pose to aquatic resources, including aquatic life and 
aquatic-dependent wildlife populations and communities. Specific research plans then identify 
and describe NHEERL’s priority research areas, linkages among the areas, and specific research 
projects which include the types of products that can be expected over the next several years. 
Although this document outlines the research that will be conducted within these research areas, 
it does not prioritize work across research areas. 

This document also provides discussion points for use with other organizations regarding 
uncertainties in risk assessment techniques, potential collaborative research, and other 
interactions. 

The scope of the document is defined by NHEERL’s research areas under the Government 
Performance and Results Act (GPRA), Goal 2, Clean and Safe Water, sub-objective 2.2.3: 
"Provide the means to identify, assess, and manage aquatic stressors, including contaminated 
sediments." This Goal is one of 10 EPA strategic goals that cover the programmatic needs of the 
Agency. NHEERL’s research under Goal 2 focuses on the development of stressor-response 
models for habitat alteration, nutrients, suspended and bedded sediments, and toxic chemicals; 
and on the development of diagnostic methods that are applicable up to and including the 
watershed scale. 

This document does not include work on some aquatic stressors (e.g., invasive species, microbial 
pathogens, and climate change), monitoring under the Environmental Monitoring Assessment 
Program (EMAP), many aspects of biocriteria, or some work related to the aquatic stressors 
effort, which is covered under other GPRA goals (e.g., molecular and cellular mechanisms of 
toxic chemicals). Research on invasive species is being conducted elsewhere as EPA is an active 
member of the Inter-Agency Invasive Species Advisory Council, which includes the Fish and 
Wildlife Service (FWS), National Oceanographic Atmospheric Administration (NOAA), and 
other federal agencies. Monitoring under EMAP and biocriteria research occurs under GPRA 
Goal 8 and other research issues are covered under other NHEERL strategic goals (see 
http://www.epa.gov/nheerl/). 

Aquatic stressors research will require continuing interaction between the Office of Research and 
Development (ORD) Laboratories and Centers, EPA’s Program Offices, and Regions, to ensure 
that the efforts are not duplicated and that the approaches developed are compatible with those 
for exposure and risk characterization. 


1 


Programmatic Needs 

EPA recognizes the need to advance risk assessment knowledge bases and to develop methods 
for reducing risks from aquatic stressors. The immediate focus is to develop and improve 
ecological criteria and diagnostic capabilities for managers, to help them meet designated uses, 
and to develop options for protection and remediation efforts. Three elements provide the 
regulatory context for NHEERL’s aquatic stressors research: 

• First, the Clean Water Act (CWA) provides the legislative mandate to restore and 
maintain the chemical, physical, and biological integrity of the Nation’s waters. 

• Second, to fulfill this mandate, EPA has established under GPRA Goal 2, sub-objective 
2.2.3, requirements for establishing ecological criteria that protect use designations for 
the Nation’s aquatic resources. Research directed toward this goal will be linked to 
Annual Performance Goals (APGs) and Measures (APMs), which NHEERL has 
identified for 2002-2008 (see Sections 4-8). 

• Finally, the Administration’s Clean Water Action Plan (CWAP) establishes key actions 
focused on watershed, wetland, and stream corridor protection and restoration; nutrient 
assessment and criteria development; and development of a contaminated sediment 
strategy (EPA 1998). 

A response to these mandates requires the development of research approaches and products that 
enhance the Agency’s capabilities with respect to the management of aquatic resources. 

References 

EPA. 1998. Clean Water Action Plan. EPA 840-R-98-001. 


2 


Section 2. 

Research Approach 


Context for Research 

The common management goal for all aquatic ecosystems is to maintain ecological integrity by 
protecting aquatic systems against degradation of habitat, loss of ecosystem functions and 
services, and reduced biodiversity. To this end, environmental managers must be able to: 1) 
assess the condition of an aquatic resource and determine the degree of impairment, 2) diagnose 
the causes of impairment, 3) forecast the effects of changes in stressor levels, and 4) develop and 
implement remediation and maintenance strategies. The first step in this process is to assign a 
designated use for a water body and then to apply the available chemical and biological criteria 
necessary to protect the use. If a resource does not support the designated use, the cause of the 
impairment must be diagnosed. 

To accomplish these tasks, managers must be able to make proper assessments, know the 
appropriate reference conditions against which to compare their assessments, have the diagnostic 
tools necessary to ascertain causes, and understand specific aquatic systems well enough to 
forecast the effectiveness of potential remediation processes. While other ORD Laboratories and 
Centers will make important contributions to GPRA Goal 2, sub-objective 2.2.3, NHEERL will 
focus on conducting ecological effects research relative to steps 1-3 above. 

The focus on aquatic stressors such as habitat alteration, nutrients, suspended and bedded 
sediments, and toxic chemicals is consistent with recent scientific consensus, recognizing that 
these undeniably widespread concerns have the potential for tremendous impact on aquatic 
ecosystems (e.g.. National Research Council [NRC] 1993, Naiman et al. 1995, Vitousek et al. 
1997, EPA 1998, NOAA 1999, EPA 2000a). Because of these stressors, an aquatic resource 
often fails to meet its designated use. States and Tribes commonly report these stressors as part 
of their Section 303(d) lists under the CWA, thus requiring the development of total maximum 
daily loads (TMDLs). Therefore, managers need a decision support system to discern the 
probable causes of impairment and to identify remediation action that will restore and protect the 
resource. 

Research Process 

Effective management and protection of aquatic resources requires multiple research elements. 
The research process for developing these elements and the products of the research are shown in 
Figure 1. This process presents a generally linear research sequence, although some research 
elements will be conducted simultaneously. State and Tribal management agencies can protect 
the ecological integrity of aquatic ecosystems only through appropriate management action, but 
NHEERL can help by providing methods and tools for assessing conditions, diagnosing 
impairments, and forecasting changes. 


3 


Research Process 


Research Products 



Figure 1. Research process and products for meeting the goal of effective management 
and protection of aquatic resources. 


Protection of the ecological integrity of aquatic ecosystems must begin with a quantification of 
the inherent properties of aquatic and aquatic-dependent habitats which are critical to the support 
of important fish, shellfish, and aquatic-dependent wildlife populations (box la). Research in 
this area will help to assess the life support functions of habitats and habitat complexes and 
provide methods to predict biological effects which result from habitat alteration (box lb). 

EMAP contributes, in part, to these basic needs under GPRA Goal 8. Ecological characterization 
and identification of the priority aquatic research elements are key to developing stressor- 
response relationships within each of the aquatic stressor areas (box 2a). Stressor-response 
relationships are needed to quantitatively assess effects over a range of foreseeable conditions of 
the stressor. These relationships provide the fundamental information required to define 
response thresholds or other patterns and to improve criteria. Determining stressor-response 
relationships also should help define symptoms of a problem and identify diagnostic measures 
that can be broadly applied. This research will provide stressor-response models, if needed, 
within each of the aquatic stressor areas (box 2b). Eventually, these models must be capable of 
dealing with multiple stressor interactions if they are to support the development of approaches 
that allow characterization of the ecological condition of aquatic systems relative to a desired 
condition. However, initial research will focus on the stressor-response relationships of the 
individual stressors, in order to set the stage for the more difficult problem of dealing with 
multiple stressors in the longer term. 


4 




































As stressor-response relationships are determined, research will be directed towards developing 
diagnostic approaches (box 3a), which will provide tools (box 3b) for building a decision support 
system. Resource managers then can use the system to assess the condition of a water body, 
diagnose the causes of any demonstrated impairment, and predict the results of any corrective 
actions that might be needed. 

Stressor-response relationships can be specific to different classes of systems. Thus, research 
also will focus on developing ecosystem classification approaches (boxes 4a,b) that allow for 
reasonable extrapolations of diagnostic approaches and stressor-response models. Classifying 
ecosystems is valuable for two primary reasons: 1) grouping ecosystems according to similar 
criteria and 2) spatially classifying ecosystems that are connected via stressor actions to facilitate 
an effective means for managing the consequences of stressors. Since little is known about scale 
relative to ecosystem classification, effects research also will provide guidance about the most 
appropriate scale for various ecosystem classification approaches, up to and including the 
watershed scale. 

At the same time, research identified in boxes la-4a will result in methods and approaches for 
deriving criteria (boxes 5a,b) for protecting aquatic ecosystems. Existing approaches, based on 
laboratory tests, have focused on individual aquatic life and wildlife species. However, much 
uncertainty is associated with extrapolating data to predict safe levels for populations and 
communities exposed to individual and multiple stressors (physical and/or chemical). Therefore, 
we need to improve current criteria, methods, or approaches for some stressors where major 
uncertainty exists, or develop them for others where little information is known (see Sections 4- 
8). In some cases, research will lead to relatively short-term fixes (1-2 years) to existing 
guidance. In others, research conducted over the longer term (3-6 years) will result in methods or 
models useful for deriving criteria with associated uncertainties. 

All aquatic stressor research elements (boxes 1-5) thus combine to help improve the tools 
available to managers for meeting designated uses (boxes 6a,b). It is important to recognize that 
NHEERL research on aquatic stressors supports the development of protective criteria, although 
actual criteria and management strategies fall beyond the research responsibilities of NHEERL. 

A discussion on a decision support system for using the products of this research follows. 

Decision Support System 

A general approach that a resource manager might follow for managing water bodies is outlined 
in the left side of Figure 2. The assessment of the condition of an aquatic resource to support 
ecological use designations first requires ecological criteria and a reference condition with which 
to compare those criteria. As shown in the right side of Figure 2, GPRA Goals 2 and 8 research 
products will support the development of both chemical and biological criteria that can assist 
managers in determining if designated uses are met. 

If designated uses are not met, managers will require a means of identifying the stressors causing 
the impairment. EPA’s Stressor Identification (SI) workgroup has developed an example of a 
"diagnostic tree" approach for SI that can be used by resource managers (EPA 2000b). NHEERL 
research will contribute by determining stressor-response relationships and specific diagnostic 


5 


indicators for aquatic stressors and by developing additional decision support tools as needed. In 
this approach, the SI process is iterative, usually beginning with retrospective analysis of 
available data, and includes the identification of stressors that might be causing the impairment. 



Figure 2. Manager’s decision support system to protect and restore aquatic resources 
using ORD’s research products (SI box is modified from EPA 2000b). 


Stressor identification consists of three main steps, the core of the SI process: 1) listing candidate 
causes of impairment, 2) analyzing these candidate causes, and 3) producing a causal character¬ 
ization. The support system also involves interactions with decision makers and stakeholders to 
assist in forecasting the effects of the stressors and in taking remediation action, if needed. 
Remediation requires, first, criteria for what is acceptable in a given environment, and second, 
the models necessary to link changes in stressors with improvements in the system. Additional 
information is provided in Section 8 (Diagnostics) of this document concerning the development 
of a framework for a decision support system. NHEERL-generated products from this research 
will be combined with exposure models and with restoration and remediation techniques 
developed by the National Exposure Research Laboratory (NERL) and National Risk 
Management Research Laboratory (NRMRL), respectively, to meet management needs. 


6 























References 


EPA. 1998. Water quality criteria and standards plan - priorities for the future. EPA 822-R-98- 
003. 

EPA. 2000a. OW/ORD Strategic Planning Research Coordination workshop document, version 
2. January 31. 

EPA. 2000b. Stressor identification guidance document. EPA-822-B-00-025. OW/ORD, 
Washington, DC, December. 

Naiman, R.J., Magnusen, J.J., McKnight, D.M., Stanford, J.A., eds. 1995. The Freshwater 
Imperative: a Research Agenda. Island Press, Washington, DC. 

NOAA. 1999. National estuarine eutrophication assessment: a summary of conditions, historical 
trends, and future outlook. Draft. National Ocean Service, Silver Spring, MD, June. 

NRC. 1993. Managing wastewater in coastal urban areas. Washington, DC. 

Vitousek, P.M., Aber, J.D., Howarth, R.W., Likens, G.E., Matson, P.A., Schindler, D.W., 
Schlesinger, W.H., Tilman, D.G. 1997. Human alteration of the global nitrogen cycle: sources 
and consequences. Ecol. Appl. 7:737-750. 


7 


Section 3. 

Research Products and Implementation Plans 


Five main products from this research have been identified for meeting the goal of protecting 
ecological integrity of aquatic systems (Figure 1, boxes lb-5b). These are: 

• Methods to predict biological effects of habitat alteration; 

• Population, community, and ecosystem stressor-response models; 

• Diagnostic tools to determine impairment or causes of impairment to aquatic systems; 

• Classification approaches to aid in the prediction and management of problems; and 

• Methods and models to support the development of ecological criteria. 

Although research to produce these products might need to be conducted independently at first, it 
also will have to be coordinated so that these products can be integrated into a manager’s 
decision support system. In some cases, these products have been established, but need to be 
improved. In others, they will have to be developed. Therefore, research is being planned 
around compatible approaches, scales, critical areas, and geographic regions, when possible. 

The remainder of this document (Seaions 4-8) provides the plans for implementing research in 
each of NHEERL’s priority areas. Each plan describes the problem the Agency faces, the goals, 
critical path for conducting the research, specific research projects to be conducted, a gap 
analysis, and references. The research projects within each plan contain the general objectives, 
scientific approach, products, benefits of the products to the client, and an estimate of the 
workload (in full time equivalents, FTEs) it will take to complete each project. 

Currently, the work groups identified at the beginning of this document along with other staff 
within NHEERL’s Ecology Divisions are detailing the projects outlined in the implementation 
plans in Sections 4-8 (Note: these detailed projects plans are not included in the present 
document). Each project will describe further coherent portions of the critical paths conceived to 
meet the goals stated in each implementation plan and will show how the projects will be 
completed to produce the products listed. Cross divisional projects will be conducted by 
scientists from more than one Ecological Division, while others will be conducted by staff of a 
single Division. Each project will consist of a design narrative (design, approach, and analysis) a 
Quality Assurance (QA) Plan, Health and Safety and Environmental Compliance Plan, and an 
Animal Care and Use Plan (if required) consistent with the Ecological Divisions’ project plan 
documentation, and will undergo internal review prior to final approval. Work load (FTEs) and 
staff also will be provided in more detail than what is stated in the implementation plans shown 
in Sections 4-8. 

The completion of research outlined in this document will require continued interaction within 
and across NHEERL’s Ecological Divisions, between ORD’s Laboratories and Centers, and 


8 


within EPA’s Program Offices and Regions. Collaboration with OW, Regions, States, and 
Tribes will be essential to ensure that this research directly supports regulatory mandates. In 
addition, it will be essential to integrate the research with present and future extramural 
initiatives (including EPA’s Science to Achieve Results [STAR] program) to ensure that ORD- 
sponsored research complements in-house programs and to fill research gaps that have been 
identified within each of the implementation plans. Many of these interactions are outlined in 
Sections 4-8; others will be delineated further in the detailed research projects that are now being 
developed by NHEERL staff. 


9 


Section 4. 

Implementation Plan for Habitat Alteration Research 


Problem 

Significant improvements in some aspects of the North American environment have been 
realized over the past several decades, but the continuing increase in human populations and 
associated activities has created an array of regulatory and policy challenges (e.g., land-use 
changes, hydrologic modification, climate change, altered biological diversity, introduction of 
normative species, concern about ecological sustainability, and cumulative effects of manmade 
chemicals) that defy traditional command/control approaches (EPA 1999). Many anthropogenic 
activities exert their influence on biota via effects on habitat, and habitat alteration is arguably 
the most important cause of declines in ecological resources in North America (EPA 1990). 

Thriving populations of fish, shellfish, and wildlife are valued by the public, not only for 
commercial, recreational, and aesthetic reasons, but also as tangible and visible surrogates for the 
overall condition of the environment. Habitats essential to the well being of these species are 
rapidly being affected by a myriad of land-use activities. Habitat alterations have been identified 
as a major cause of endangerment for species within the United States. For example, the U.S. 
has the most diverse temperate freshwater fish fauna in the world, but 35-40% of its 790 fish 
species are imperiled because of poor land use practices, wetland alteration, introductions of 
exotic species, and other habitat-altering factors (Warren and Burr 1994, Stein and Flack 1997). 

EPA has not traditionally focused its research, regulatory, or policy effort on habitat alteration. 
However, a number of factors converge to justify a new EPA emphasis on habitat issues. The 
CWA has a goal "to restore and maintain the physical, chemical, and biological integrity of the 
Nation’s waters," including the "protection and propagation of fish, shellfish, and wildlife." 

While the chemical integrity of aquatic resources is much improved, physical and biological 
integrity remains a concern. Habitat alteration is a common cause for the failure of aquatic 
systems to meet designated uses as required by the CWA, and addressing these failures 
increasingly requires ameliorating the cumulative impacts of diffuse stressors including nutrient 
loading, sedimentation, and altered hydrologic regime. The necessary integrated approach to 
environmental protection is perhaps best provided by habitat-based criteria. As required by the 
Endangered Species Act (ESA), EPA is increasingly being asked to participate in interagency 
species protection and restoration efforts where habitat issues play a key role. Because one of 
EPA’s core ecological regulatory authorities is the CWA, the species endpoints for which habitat 
alteration is of greatest concern are aquatic species (i.e., fish and shellfish), and water- and 
wetland-dependent wildlife. By focusing on aquatic ecosystems and habitats supporting species 
of combined ecological and societal importance, EPA can advance broad environmental 
protection goals while directly addressing issue-driven stakeholder concerns. 

EPA’s Office of Water (OW) has identified priority ecosystem types for which habitat alteration 
research is especially needed. These systems include freshwater and estuarine wetlands, stream 
corridors, and marine and Great Lakes coastal zones. Not coincidentally, these resource types are 
of considerable importance in sustaining ecologically and societally valuable fish, shellfish, and 


10 


wildlife populations. More than 50% of U.S. marine fisheries exploit species that are estuarine- 
dependent at some life stage, and many estuarine fisheries are in decline due to combined effects 
of over fishing, habitat alteration, and pollutants (Houde and Rutherford 1993). Virtually all 
Great Lakes fish depend at least indirectly on coastal wetland habitats, where habitat alteration is 
an important threat (Whillans 1992). Within these priority ecosystem types, OW has a particular 
interest in vegetated habitats. Aquatic vegetation is not only a key habitat for many wetland, 
estuarine, and coastal species, but also a key mediator of stressor effects on aquatic biota and a 
primary response variable for anthropogenic stressors such as nutrient and sediment loading. The 
combination of OW interests, pressures affecting societally and ecologically important ^cies, 
and NHEERL research expertise leads to a focus on these endpoints in the this plan. 

Assessing the ecological consequences of habitat alteration has been called one of the most 
challenging scientific problems and environmental policy issues confronting society (NRC 1997, 
Rapport et al. 1998, EPA 1990). The importance of habitat quality and quantity for maintaining 
species is indisputable, but quantifying exactly how species depend on habitats is multi-faceted 
and complex. Habitat provides a wide array of species life-support functions, ranging from 
providing shelter, substrate, and appropriate physiological conditions; to mediating natural 
disturbances and anthropogenic stressors; to maintaining food webs by hosting primary and 
secondary production. Consequently, habitat alteration can degrade diversity, food-web 
structure, ecosystem function, and populations of valued fish, shellfish, and wildlife species via 
complex effect pathways. Mobile and migratory species can use multiple habitats to meet 
developmental requirements or sustain local populations, and "habitat” for them may refer to a 
combination of quantity, quality, extent, and arrangement of different habitat types at a variety of 
spatial scales. Many stressors interact with aquatic systems in ways that alter the normal spatial 
distribution or mosaic of habitat patches, with important implications for ecosystem function and 
dependent fish, shellfish, and wildlife populations. More generally, successful preservation of 
biological diversity and ecosystem structure and function requires protection of multiple habitats 
within a landscape framework and not merely individual habitats in isolation. For many 
important aquatic habitats, there is little quantitative information on the relationship of habitat to 
dependent biota; in particular how changes in habitat quality influence the well being of fish, 
shellfish, and wildlife populations. Finally, broad biogeographic gradients affect the responses of 
ecosystems and biota to habitat alteration. For all these reasons, it is a significant research 
challenge to quantify the life support functions of specific habitats and habitat complexes in 
sufficient detail to predict the biological effects of both incremental and catastrophic habitat 
alteration. 

NHEERL has initiated a nationwide research program to quantitatively link alterations in key 
habitats to fish, shellfish, and wildlife endpoints because habitat alteration is such an important, 
pervasive stressor on valued aquatic resources. The research involves all four Ecological 
Divisions of NHEERL and spans the coastal resources of the East, West, Gulf states, and the 
upper Midwest. NHEERL will enter into partnerships with other management agencies and 
research entities as appropriate to further these research goals. The research described in this 
plan will help build the scientific basis to implement regulations and policies to protect aquatic 
populations and the ecosystems upon which they depend. 


11 


Goals 


The overarching goal of this plan is to provide the scientific basis for assessing the role of 
essential habitat in maintaining healthy populations of fish, shellfish, and wildlife and the 
ecosystems upon which they depend. 

Such a scientific understanding is essential in devising habitat protection and restoration 
priorities and schemes. The spatial pattern and temporal dynamics of habitats and habitat 
conditions within landscapes play significant roles in the long-term viability of aquatic 
populations, species assemblages, and ecosystem functions. Ecosystem level responses to 
stressors are functions of both the tolerance of individual species and habitats to those stressors, 
and the spatial distribution and connectivity of habitats within the landscape. Furthermore, 
habitat components themselves play ecosystem roles that mediate the response of species and 
assemblages to stressors. Finally, habitat influences populations and assemblages at hierarchical 
spatial scales ranging from patches (micro-scale) to entire ecosystems (macro-scale) to 
watersheds or regions (landscape scale). Understanding how effects of anthropogenic stressors 
are mediated by alteration in habitat quality, abundance, and configuration at various spatial 
scales is necessary in order to develop aquatic resource protection criteria and to predict the 
resiliency, restorability, and recovery of fish and wildlife populations and their supporting 
ecosystems. 

More specifically, APGs and APMs for the research are: 

APG 1 FY02 Provide suites of relevant fish, shellfish, and wildlife species endpoints suitable for 
setting regional-scale habitat protection criteria for coastal systems, along with preliminary 
reviews of methods, modeling approaches, and available data for relating habitat alteration to 
changes in those species. 

APM 1A FY02 Listings of the high-priority species of fish, shellfish, and aquatic- 
dependent wildlife for study in each biogeographic region, and listings of the habitats that 
are considered to be critical to each (WED). 

APG 2 FY04 Provide models for linking habitat alteration stressors and mercury to the regional 
problems of Northeast Loons and to landscape-watershed alterations for Pacific salmon. 

APM 2A FY03 Prototype watershed-stream network modeling approach for Pacific 
salmon (WED). 

APM 2B FY04 Habitat suitability indices to support population models for projecting 
relative risks of multiple stressors including toxic chemicals and habitat alteration to 
common loons (AED). 

APG 3 FY04 (GPRA # 8) Provide demonstration stressor-response relationships and/or models 
linking loss and alteration of habitat to selected fish, shellfish, and wildlife endpoints. 

APM 3A FY03 Penaeid shrimp dependence on seagrass habitat (GED). 


12 


APM 3B FY03 Finfish dependence on seagrass and oyster reef habitats (GED). 

APM 3C FY04 Report characterizing the reFationship between habitat in stream networks 
and salmon-native fish for coastal Oregon watersheds (WED). 

APM 3D FY04 Report characterizing the relationship between alteration of vegetated 
habitats and nekton use of those habitats (AED). 

APM 3E FY04 Report characterizing relationships between multiple habitat types and 
economically valuable fish at the scale of an estuarine shoreline (AED). 

APM 3F FY04 (GPRA # 58) Preliminary report characterizing relationships between 
abundance, quality, and arrangement of various habitat types and selected biotic 
assessment endpoints in coastal systems (WED). 

APG 4 FY05 Provide indices of patch, ecosystem, and landscape-scale habitat integrity based on 
support for selected fish, shellfish, and wildlife assemblages. 

APM 4A FY05 Develop indices of watershed integrity based on land use/land cover and 
relationships to fish (WED, MED). 

APG 5 FY06 Provide stressor-response relationships and/or models linking loss and alteration of 
habitat to selected fish, shellfish, and wildlife endpoints. 

APM 5A FY05 Reports characterizing the relationship between landscape-scale habitat 
mosaics and native fish by wetland type in the Great Lakes (MED). 

APM 5B FY06 Report characterizing relationships between abundance, quality, and 
arrangement of various habitat types and selected biotic assessment endpoints in coastal 
systems (WED, AED, GED, MED). 

APG 6 FY08 Provide suites of habitat alteration-biological response relationships and 
generalization/extrapolation schemes suitable for developing broad-scale habitat criteria for 
streams and coastal systems, and provide approaches for evaluating combined effects of habitat 
alteration and other stressors. 

APM 6A FY04 Ecological consequences of marine derived nutrients and nutrient 
enrichment for aquatic biota and stream habitat quality, with an emphasis on salmon and 
native fish (WED). 

APM 6B FY07 Regional models of landscape influence of salmon/native fish in the 
Pacific Northwest and native fish in Great Lake coastal wetlands (WED, MED). 

APM 6C FY08 Synthesized quantitative species-habitat relationships suitable for 
developing regional habitat-based biocriteria for shorelines, lakes, and estuaries (AED, 
GED, MED, WED). 


13 


APM 6D FY08 Interactions between stream nutrients and habitat alteration on water 
quality and aquatic life (WED). 

Critical Path 

Determining the role of essential habitat in maintaining fish, shellfish, and wildlife populations 
involves two distinct research components: 

1. Describing the relationship between habitat and biota at appropriate spatial scales and with 
sufficient detail and resolution to quantify the effects of both incremental and catastrophic habitat 
alteration; and 

2. Synthesizing the cumulative support function of individual habitats and ecosystems and 
integrating habitat alteration effects with effects of other stressors, so that resource protection and 
restoration priorities can be evaluated at spatial scales up to and including regions or large 
receiving bodies. 

Within these two research components, research efforts can be further categorized by spatial 
scale, ranging from habitats within ecosystems to entire ecosystems to landscapes and regional 
habitat mosaics. A critical path diagram illustrating this two-component research strategy and 
the spatial focus of research efforts is given in Figure 3. Table 1 gives a overall time-line for the 
overall plan. 

Component 1 

NHEERL proposes to focus research describing relationships between habitat and biota on 
coastal marshes, estuaries, and nearshore environments. Two primary spatial scales will be 
considered: the scale of habitat elements, especially vegetated habitat, within marsh and 
estuarine systems; and the scale of entire coastal wetland and estuarine ecosystems (i.e., micro 
and macro-habitat scale). Synthesizing the cumulative population support functions of coastal 
habitats and ecosystems on a regional basis is a long-term goal, but the initial emphasis will be 
on quantifying habitat-biota relationships at the ecosystem and within ecosystem scale. 

Necessary elements of this habitat research include: 

1. Identifying suites of endpoints that provide a nationally comprehensive and comparable basis 
for linking alteration of key coastal habitats to species and assemblages of economic or societal 


14 




Habitat alteration - biota response Methods for integrating habitat alteration and 

relationships suitable for setting habitat other stressors in a regional or landscape 

criteria and protection/restoration priorities level effects context 


t t 


Extrapolation across ecosystems requires 


Evaluation of the relative & cumulative 

classification & regionalization schemes and 


effects of stressors including habitat loss on 

datasets [APGS, 6] 


spatially structured populations [APGS, 6] 

A 

T 

Habitat integrity indices based on habitat - 
biota response relationships [APG4] 

t 


t 

Landscape scale habitat integrity indices 
[APG4] 

1 

Field work, data analysis, and modelling to 
develop habitat - biota response relationships 
& mechanisms [APGS] 

t 


t 

Landscape scale heibitat - biota response 
relationships & mechanisms [APG3] 

1 

Review available data & models for linking 
habitat to biotic endpoints [APGl] 


t 

Models and approaches for assessing 

Identify species, ecosystems, and habitats of 
concern [APGl] 


importance of spatial structure and 
connectivity [APG2] 


t t t t 



Project 1 
vegetated 
aquatic habitat 


Project 2 

coastal 

ecosystems 


Project 3 
sahnonids / 
native fishes 


Project 4 

piscivorous 

birds 


Component 1: habitat biota 
relationships (habitat and 
ecosystem scale) 


Component 2: scaling up to the 
landscape (reach, network, 
watershed, region) 


Figure 3. Critical path for habitat alteration research (APGs) refer to those listed and 
described in the Goals subsection. 


15 







Table 1. Time line for habitat alteration research. APGs in the table are abbreviated versions of those listed under the Goals 
subsection, and arrows indicate which research components support which APGs. "HAPR" refers to habitat alteration-population 
response models. 


00 

o 


p- 

o 

>- 


o 

o 

>- 


o 


c 




11 

o C 
P* o 
"7: 'Z. 

U CO 

:£ -S 

c - 

2" 

< § 


a. 

Cl 

?3 


l/', 

c 

a. 

< 


in 

■J '2 

"cs ■ — 

o 


CSj 

i) 


< .E 


<o 

a 


cn 

Q. 


V. 

C 


O S p 
a. c — 
< S 


<u 

op 

C U 


v: 

(S 


w c 

^ ll . 
P £ Cl. 


<n 

•E 

'G 

4> .r: 

c. — 


< 


-- > 

7. 2 


O 

a- 

< 


in 

o 


3 

V5 


a. 

"O 

c 

<u 

in 

.2 

'tj 


es 

> 

4> 

V 

</9 

3 

« 

io 

cs 

x: 

I 

in 

O 


O 


m 

o 


C/C 

<D 


O 13 


— 

■g 

£ 

a: 

a, 

< 


g. 8. 


o 


J 5s 


o 


i ^ 

■5b ^ 

u u 

- 1 
« B 

p ^ 

X 

o 

■w 

ea 
TJ 


OS 

> 


o 

> 

4> 

•o 

o 

1 

2 
13 
ea: 


so 

O 

Cu 

< 

wo 

o 

a. 

< 


in 

.S' 

!£ 

C/C 

c 

_o 

a- 

< 

X 


c/c 

U 

JZ 

c 

>, 

c/c 


y ^ 

8 . > 


5 2 


in ea 

« > 

.3 4> 

in 


Q « 

I 

S M 


in 

U 


in 

O 


SO 

o 

a- 

< 

wo 

o 

C-, 

< 


c/c 

C 

O 


•a 

c 

ca 

CO 

o 


c/c 

U 


.2 

c/3 

O 

■g 

£ 

a- 

< 

X 


I I 


% 

wo 

o 


t 

■g 

•g 

y 

c 




y 

"g 

£ 

m 

y 


3 

£ 


ro 

o 


y 

■g 

£ 

Ct:; 

a. 

< 


^ -5 = 


wo 

O 

% 


c/c 

a. 

Ic 

MC 

c 

_o 

ea 


O 

CA 

’q3 

y 

■S 

CA 


y 

« 

2 

la 

> 

"q. 

2 

y 

> 

y 

T3 


1 

2 
y 


cn 

O 


I % 


O ^ 


cs 

O 

C 

*Zo 

c/3 

03 


15 

JC 

g. 

ea 

y 

in 

T3 

C 

c 3 


a. 

o 

•■am 

y 

> 

y 

■X3 

13 

■g 

£ 

u. 

i 


j g 


o 


wo 

O 


t 

<u 

■g 

3 

y 

o 

c 

y 

3 

5= 

c 

'i. 

03 

g 

T3 

C 

ea 

■« 

c 

o 

‘Sb 

y 


t " 


CA 

y 

o 

c 


y 


ro 

o 

a. 

< 


y 

g 

£ 

ac 

a- 

< 

MM 

D 

P ^ 
g 

cti 

"O 
c 
ea 


^ P 

t o. 
< 

f t 

g » 

I ■§ 

^ I 

S a 

^ Cu 

"O c/3 

i 

£ o 


3 

O. 




y 


< 


g 

w' 

y • 


CA 

c 

o 


y 
CL 
y ea 


5 y 


y 

s 

c. 




<N 


ea 

5 

in JC 


in 

C. 


■y. 


y ea w 


CM 


D 

C 


y y -3 y 
y V, XI i_ 


y 


c =« 

C </: 

CA "O 

O. 'c 


ca 
y 

CA 

■a 

3 ea 

- CA CA 


CA 

<A C 

y ^ 


ca 


CM 

C 

y 

c 

O 

D 


U 


■ > wc 

bi "E 


16 

























relevance, and identifying data and models available to make these linkages. 

2. Devising assessment and measurement endpoints and strategies for the fish, shellfish, and 
wildlife populations and habitat elements of concern so that measurements are comparable or 
complementary and informative across regions, ecosystems, and habitats. 

3. Developing habitat alteration-population response relationships for the species and habitats of 
concern capable of quantifying effects of both incremental and catastrophic habitat alteration. 

The relationships must deal with both individual habitat components and interactions among 
them. Habitat-biota relationships may require intermediate steps to properly describe the linkage 
pathways (e.g., to capture the productivity subsidy to fishes from coastal wetland vegetation). 

4. Devising regionalization and classification schemes reflecting the range and distribution of 
coastal habitats and ecosystems to identify biogeographic expectations and capture ecosystem 
constraints and forcing factors. 

5. Identifying data sets and approaches for extrapolation, both to specific unstudied systems, and 
to make inferences for the population of systems from the suite sampled. 

Component 2 

NHEERL plans to pursue the influence of human activities on habitat at landscape, regional, and 
watershed scales via two conceptually linked,studies. One project will examine watershed and 
landscape scale habitat issues affecting recovery of Pacific Northwest salmon, and fishes reliant 
upon Great Lakes coastal wetlands. A second project will examine the interaction of a suite of 
anthropogenic stressors including habitat alteration affecting piscivorous birds (e.g., common 
loons) distributed across heterogeneous lake districts. Unlike the wetland/nearshore research 
described above. Component 2 research projects emphasize modeling and Geographic 
Information Systems (GIS), and explicitly consider the interaction among habitat alteration and 
other stressors. Common components of these research projects include: 

1. Identifying data sets, approaches, and measurements for characterizing the factors, including 
habitat alteration and other stressors that affect selected species or assemblages at large or 
hierarchical spatial scales. 

2. Developing and comparing approaches, indices, and models for extrapolating from individual 
or local habitat-biota relationships to effects on regionally-distributed populations or 
metapopulations. 

3. Assessing the importance of spatial structure and connectivity of habitats via modeling efforts 
at varying spatial scales and resolutions. 

Research Projects 

This plan is divided into four parallel but closely linked projects. Project 1 {Coastal Vegetated 
Habitat Research) addresses societally important endpoints of concern that are affeaed by 


17 


alteration of critical habitats, especially vegetated aquatic habitats, within coastal ecosystems. 
Project 2 {Shoreline, Lake, and Estuary Scale Habitat Research) also deals with societally 
important endpoints, but focuses on those coastal ecosystems where the interactions of multiple 
habitats predominantly determine the condition of fish, shellfish, and wildlife populations. 
Project 3 {Salmon arid Native Fish Habitat Research) and project 4 {Multiple Stressor Risks to 
Common Loon and Other Piscivorous Bird Populations [cross-listed in Section 7, Toxic 
Chemicals, Project B3\) concentrate on providing the scientific basis to protect critically 
important endpoints such as wild salmon and migratory wildlife whose populations are at risk 
due to, among various factors, large scale changes in their habitats. As described in the Critical 
Path, work at the vegetated habitat and multiple habitat scale primarily addresses the need for 
developing habitat-biota-response relationships. Work at the watershed/regional/landscape scale 
is divided into two projects that are conceptually related but deal with different ecosystems and 
biological endpoints. Research will directly address the consequences of habitat alteration for 
societally important fish, shellfish, and wildlife species. Table 2 lists those species that, on the 
basis of initial assessment, appear as the best candidates for study under projects 1 and 2. During 
development of specific research plans by NHEERL Ecological Divisions, a more detailed 

Table 2. List of candidate species for study in marine and Great Lakes coastal regions. 


Northeast Atlantic Coast 
{Atlantic Ecology Division) 

Gulf Coast 

{Gulf Ecology Division) 

Winter flounder 

Striped bass, bluehsh, and weakfish 

Tautog 

Bay scallops and lobster 

Waterfowl and shorebirds 

Penaeid shrimp 

Blue crab 

Red drum and other sciaenids 

Oysters 

Waterfowl and shorebirds 

Great Lakes 

{Mid-Continent Ecology Division) 

Northwest Pacific Coast 
{Western Ecology Division) 

Northern pike 

Walleye 

Yellow perch 

Laigemouth and smallmouth bass 

Waterfowl and shorebirds 

Salmon and trout 

Dungeness crab 

Pacific herring 

Threatened and endangered native fishes 
Waterfowl and shorebirds 


analysis of the current state of knowledge concerning the species-habitat relationships for those 
on this list will be done. Development of research efforts on particular organisms under this plan 
will be closely coordinated with other efforts, both within EPA and in other organizations. For 
example, NHEERL also has developed a Wildlife Research Strategy (WRS) (EPA 2000), and 
the related research initiated here will be integrated with any NHEERL research on semi-aquatic 
wildlife. 

A systematic approach will be used to evaluate candidates and select species using a consistent 
societal value-based scheme. A data table will be constructed for each region listing the species 


18 







and their associated societal values or characteristics, such as economic value, charismatic value, 
stock status, and extent of coastal/estuarine dependency. Species will then be prioritized. 
Development of a final list from the top-priority populations will be used to select species from a 
range of life histories and ecological niches to best represent biological diversity and ecosystem 
flmction. The final list also will include information that will consider the scientific feasibility of 
studying populations, because species with certain life histories may be very difficult or 
impractical to study given the available research tools. For some species, researdi often will 
focus on the juvenile stages because Juveniles are typically more dependent on specific habitats 
and are not subject to direct fishing pressure, but adults will sometimes be the endpoint that best 
integrates over the suite of habitat elements being considered. Secondary species endpoints for 
each of the above areas are the key forage fishes and invertebrates upon which larger societally 
valuable species depend, and these will be considered as well. 

Project Title 1: Coastal Vegetated Habitat Research 

Project Coordination and Resources (11.15 FTEs: AED-5.05, GED-2.0, MED-1.0, WED-3.1) 
Objectives 

• To quantify the role of aquatic vegetated habitat in providing structure and life support 
functions (e.g., food and shelter) to selected and societally important fish, shellfish, and 
wildlife populations. 

• To identify those attributes of habitat within vegetated aquatic systems that are key to 
sustaining societally important species, and to further determine the functional 
relationships between those attributes and the utilization of that habitat by primary and 
secondary (e.g., forage organisms) assessment endpoints. 

• To integrate the results of habitat-specific research results with other NHEERL habitat 
research that focuses on larger scale questions that range from localized among-habitat 
differences to the landscape and regional scale. 

• To provide to other research teams within NHEERL the functional relationships between 
aquatic vegetation attributes found to be important to endpoints, and that are also 
impacted by stressors. 

• To transmit the results of this research to resource managers in a format appropriate for 
its application in policy and regulatory decisions. 

Scientific Approach 

The definition of "habitat” invariably combines two concepts: a geographic location; and the 
flora and fauna that are regarded as dependent upon, or otherwise functionally associated, in 
some way, with that location. Aquatic habitats can range in character from bare sand, sediment, 
or rock substrates to areas of submerged or emergent vegetation. This portion of the plan focuses 
on aquatic vegetation - the areas of freshwater or estuarine wetlands, marshes, and sea grass beds 


19 


that, by virtue of their attachment to the substrate, are also associated with location. Areas of 
aquatic vegetation represent the smallest, discrete scale that research described in this plan will 
concentrate upon and will be studied at the greatest level of detail. Project 2 (Shoreline-, Lake- 
and Estuary-Scale Research) extends to larger scales, but at lesser detail, and will consider how 
vegetated habitat interacts with other habitat types to determine biotic structure at the ecosystem 
scale. 

The emphasis on aquatic vegetation in this project is appropriate for several reasons. First, 
aquatic vegetation is one of the most widespread and important types of aquatic habitat, in part 
because of the exceptional productivity of the plants. Recreationally and commercially important 
fish, shellfish, and wildlife, as well as rare and endangered species that are especially valued by 
human society, frequently exploit this productivity, either using the vegetation as a direct food 
resource, or indirectly, by feeding on smaller forage organisms that rely directly on the 
vegetation. Aquatic vegetation also strongly influences local physical and chemical habitat 
conditions of significance to fish and shellfish, including substrate type and stability, wave and 
current energy, and water quality. The structural complexity of aquatic vegetation provides 
shelter and nursery areas for its inhabitants. Overall, research will focus on evaluating the 
importance of habitat attributes of vegetated aquatic systems to the assessment endpoints of 
interest to society (see Figure 4). Aquatic vegetation is itself a key endpoint of research plans 
being formulated under the other aquatic stressors implementation plans, including Nutrients 
(Section 5) and Diagnostics (Section 8), and research outlined in this plan will be closely 
integrated with those efforts. 

Identification and Prioritization of Assessment Endpoints 

The assessment endpoints are organisms believed to be dependent on aquatic vegetation and 
identified as of societal value, and hence of regulatory and policy importance. Societal 
relevance will be the dominant criterion for assessment endpoint selection, but societal relevance 
needs to be intersected with ecological relevance and EPA research capabilities and regulatory 
mandates. Endpoints most likely will be chosen from the above listing of candidate species. 
However, other, intermediate endpoints such as forage fish species may also be required because 
the link between societally relevant species and aquatic vegetation may be mediated through 
secondary production, water quality, or other functional aspects of aquatic vegetation. During 
the development of specific research plans, the researchers participating in this effort will 
evaluate critically the suitability and appropriateness of proposed assessment endpoints with 
regard to regional EPA concerns, the laboratory capabilities, and the research activities of other 
agencies. Additionally, a concerted effort will be made to coordinate this research with that of 
other offices within ORD such as NERL and Federal and State resource management agencies. 


20 


FISH/SHELLFISH/WILDLFE 

- 

FOODWEBS 

- abundance 

- productivity 

- species conposition 

Indirect Effects: 

- ffophic structure (levds. 

-guildstructure -%exotics 

pathwQ5?s, etc.) 

- population structure 

- food abundance 

- food con^osition 

- corrpetition 


A 


Direct Effects: 

- shelter 

- spavaing substrate 

- physiolos^ limits 


Direct Effects: 

- habitat for pr^ ori^inisms 

- primary production 

- disturtence regime 


A 



HABITAT COMPONENTS VULNERABLE TO ALTERATION 

Sediments 

U^ter ciualitv 

Plants 

Morphologv 

-grain size 

-DO 

-growth form 

-area 

-% ori^nic 

-pH 

-areal erient 

-bathymetry 


-turbidity 

-biomass 

-channel vs. backwater 


-temperature 

-species 



Figure 4. Components of coastal vegetated habitat with possible pathways for direct and indirect 
effects of habitat alteration on fish, shellfish, and wildlife. 


so as to preclude duplication of effort, allow the differing objectives of the agencies to be 
represented (e.g., utilization vs. conservation), and foster synergy. As an example, both the 
South Atlantic Fishery Management Council and the Atlantic States Marine Fishery Commission 
have explicitly identified submerged aquatic vegetation (SAV) as areas of concern with respect 
to fishery resources, and EPA research efforts will be coordinated with those entities. Research 
on endangered species, such as Pacific salmonids, will necessitate close collaboration and 
coordination with other agencies, because authorization for and logistics of discrete, stand-alone 
efforts may be difficult or impossible. Additionally, a number of existing EPA or other Federal 
and State databases and software (e.g., those available at http://www.epa.gov/OW/soft.htiTil) may 
be useful during the implementation of this research. 

Assessment of Key Habitat Elements for Biota of Vegetated Aquatic Systems 

Assessing effects of incremental habitat alteration on species requires quantitative understanding 
of a given organism's reliance upon vegetated aquatic habitat. For clarity, this step is listed 
separately from the step of identifying candidate organisms, but at least a partial assessment of 
vegetation dependency will be done concurrently with species identification and prioritization. 
This step will integrate data from ongoing or published research, but EPA field efforts for some 
species, ecosystems, and habitat types will be required. It is necessary to critically evaluate the 
nature and level of the organism's dependence on vegetated habitat, both with respect to life 
history stages and the importance of vegetated habitat relative to alternative habitats. For 


21 
















example, there may be a perception that a given organism is dependent on aquatic vegetation 
when in fact it is more of a case of co-occurrence between the organism and the vegetation that 
share similar physical requirements, rather than a dependence of the organisms on conditions 
provided by the vegetation. An example would be migratory fishes, such as Pacific salmonids. 
The level to which these fishes utilize estuarine habitat in their migratory cycle is not well 
known, and the utilization of estuarine seagrass beds is thought to vary greatly among salmonid 
species and stocks. If such vegetation were not present, would salmon simply reduce their 
residence time in the estuary and continue their seaward migration sooner than they would 
otherwise? It must also be assessed whether the organism is associated with the vegetated 
habitat throughout its life, or only occupies the habitat during specific life history phases or 
seasons. It should be noted that many species have life history stages that are notoriously 
difficult to observe or sample, and it may be difficult to establish whether and how much these 
organisms are truly dependent on aquatic vegetation. 

Characterization of the Species (Assessment Endpoint)-Habitat Relationships 

In order to quantify species-habitat relationships, it is necessary to define the structural and 
functional attributes of aquatic vegetation that are to be documented and their alignment with the 
hypothesized assessment endpoint requirements. Standardization is necessary to ensure that data 
are collected in an agreed upon and consistent manner among the research efforts. There must 
also be agreement and coordination among broader research groups: aquatic vegetation is 
considered in this document from the perspective of its function as a habitat, but other research 
will be evaluating the effect of nutrient loading and other anthropogenic stressors on aquatic 
vegetation, and so the data collected for habitat and nutrient work (e.g., see Section 5) should 
support one another. Examples of vegetation attributes include species composition, areal extent, 
fragmentation or patchiness of the habitat, zonation, productivity, density, growth rates, condition 
(or other evaluations of the vegetation's "health"), degree of protection from predators the 
vegetation might afford, substrate characteristics, water quality, seasonal alterations to the 
habitat, and whether it directly provides food for an assessment endpoint or food for a secondary 
endpoint such as forage organisms. Standardization of data is highly desirable, but it must not 
neglect regional differences in vegetated habitat. For example, many marine vegetated habitats 
are spatially extensive and largely monotypic, and efforts to characterize habitats may focus 
largely on vegetation condition and extent. In contrast, vegetated habitat of Great Lakes coastal 
wetlands is diverse and variable over relatively small spatial scales, so that research efforts here 
will focus on distribution and interspersion of habitat types with the ultimate goal of deriving 
habitat evaluation procedures capable of synthesizing habitat components across entire wetlands. 

Quantifying the Consequences of Alteration of Vegetated Aquatic Habitats 

After identifying important species that depend in some way on aquatic vegetation, and having 
defined the suite of vegetation attributes relevant to those organisms, the research will then 
quantify the relationships between the two. Quantifying species-vegetated habitat relationships 
in sufficient detail to permit evaluation of both catastrophic and incremental habitat alteration 
requires developing mechanistic or empirical relationships spanning a range of habitat extent and 
characteristics. Insofar as possible, research also should identify key portions of the response 
relationships including response thresholds, maximum biological potential, and levels above 


22 


which factors other than habitat limit populations. Because ecological constraints and co-factors 
independent of vegetated habitat will also determine local biological potential, there is likely to 
be a family of curves for different ecological conditions; for example, for different salinities, 
wave energy regimes, or wetland types. Assigning habitats or systems to the appropriate 
response curve will require development and evaluation of classification systems and methods 
for determining ecological potential. This work will be done with the objective of contributing to 
the larger-scale habitat considerations (described below). Therefore, achieving broad spatial 
coverages for species-habitat relationships will require stratifying response curves by ecoregion 
or latitude, devising methods for extrapolation outside the range of measured habitat types and 
co-factors, and schemes for regionalizing population endpoints when specific populations of 
interest have insufficiently broad distributions. The balance among empirical and 
mechanistic investigations will depend on the strength of the linkage among the particular 
species endpoint and the habitat, and on the spatial extent for which relationships are desired. 

For example, situations where the primary concern is a particular species with identifiable 
demographic bottlenecks that depend directly on specific, measurable habitat elements may lend 
themselves to mechanistic species-habitat relationships. For other situations, where the 
dependence on vegetated habitat is more diffuse and specific population bottlenecks cannot be 
identified or where a suite of endpoints is applied over large regions, empirical relationships may 
be more easily obtained and appropriate. 

Products 

APM 1A FY02 Listings of the high-priority species of fish, shellfish, and aquatic-dependent 
wildlife for study in each biogeographic region, and listings of the habitats that are ccxisidered to 
be critical to each (WED). 

APM 3A FY03 Penaeid shrimp dependence on seagrass habitat (GED). 

APM 3B FY03 Finfish dependence on seagrass and oyster reef habitats (GED). 

APM 3D FY04 Report characterizing the relationship between alteration of vegetated habitats 
and nekton use of those habitats (AED). 

APM 3F (GPRA # 58) FY04 Preliminary report characterizing relationships between abundance, 
quality, and arrangement of various habitat types and selected biotic assessment endpoints in 
coastal systems (WED). 

APM 5B FY06 Report characterizing relationships between abundance, quality, and arrangement 
of various habitat types and selected biotic assessment endpoints in coastal systems (WED, AED, 
GED, MED). 

APM 6C FY08 Synthesized quantitative species-habitat relationships suitable for developing 
regional habitat-based biocriteria for shorelines, lakes, and estuaries (AED, GED, MED, WED). 


23 


Benefits of Products 


The Office of Water will be supplied with information on the effects of alteration of aquatic 
vegetated habitat on key endpoints (fish, shellfish, and wildlife populations) to support 
development of policies protective of these societally important endpoints. 

Project Title 2: Shoreline, Lake, and Estuary Scale Habitat Research 

Project Coordination and Resources (5.75 FTEs: AED-2.0, GED-0.75, MED-2.0, WED-1.0) 

Objectives 

• Identify the high-priority populations of fish, shellfish, and wildlife in each region; 
identify the habitats that are critical to these populations; and characterize the 
contributions of each habitat to life-support for these populations. Much of this objective 
will be accomplished through synthesizing the available literature. 

• Develop and validate habitat alteration-population response relationships (classified, 
quantitative models) for the identified species and habitats in each region at the scale of 
the shoreline, lake, or estuary. 

• Where feasible, develop and validate comprehensive multi-species models to predict 
quantitative changes in fish, shellfish, and wildlife resource value that would result from 
habitat alteration to a habitat-mapped shoreline, lake, or estuaiy. 

Scientific Approach (Overview) 

This subcomponent of NHEERL habitat research will focus on economically valuable and 
charismatic coastal species that use multiple habitats. Population responses will be evaluated at 
the scale of an aquatic shoreline (including shallow and intertidal habitats through deeper water 
habitats) or at the scale of an entire lake, cove, estuary, or subestuary. This approach is needed 
because many fisheries and wildlife species depend on several habitats in their life histories and 
migratory patterns. Houde and Rutherford (1993) list 21 estuarine-dependent species that make 
up more than 50% of all U.S. commercial fisheries landings, exclusive of Alaska pollock. All 21 
of these species depend on multiple habitats at one or more stages of their life histories. EPA 
needs to plan research that will examine availability and alteration of multiple habitats within 
lakes and estuaries. 

The primary goal of this work is to produce habitat alteration-population response models for 
high-priority populations of fish, shellfish, and wildlife. These habitat alteration-population 
response models will be designed to fit into spatially explicit risk assessment population models. 
A subsequent goal is to produce larger, comprehensive, multi-species models that can integrate 
single-species models to predict the total consequences of habitat alteration to a suite of 
economically valuable and charismatic species. Where vegetated habitats are involved, this work 
will be conducted with strong ties to project 1. These research plans differ in that this project 
examines all the major vegetated and unvegetated habitats at larger scales, but delivers a cruder 


24 


level of detail for the individual habitats compared to work proposed in project 2. Where overlap 
occurs, both groups of habitat researchers will work together to convey data and findings, and to 
avoid duplication of effort. This collaboration will occur at every step of the scientific approach 
described here. Other collaborations within EPA and with different Federal agencies also will be 
necessary to accomplish these research goals. These collaborations need to be actively pursued 
and nurtured from the onset of this research. 

Scientific Approach (Research Steps) 

7. Identify suites of species and habitats that are most critical in each region, and characterize 
each habitat*s contributions towards the survival of these species. 

Species for study in each region will be prioritized and selected as described above in the 
introduction to the section on Research Projects. For each selected species, key habitats will be 
identified. These habitats might be defined on the basis of "bioengineering" species (such as 
SAV, or burrowing shrimp), or on the basis of depth and substrate. It will be necessary to 
determine how important each "critical" habitat is relative to the other habitats these populations 
utilize. To this end, an understanding of how each habitat functions to support these populations 
will greatly help the development of habitat alteration-population response models. For estuarine 
and marine fishes and shellfish, the National Marine Fisheries Service (NMFS) Essential Fish 
Habitat work has already gone through this process, and summaries of the literature are readily 
available. Other compiled resources such as the FishBase Dataset, the FWS Species and 
Community Profiles, and the NOAA/NMFS Technical Memoranda will also be very useful. In 
many cases, substantial aspects of this research step can probably be accomplished by 
synthesizing the available literature. 

2. Quantify population responses to habitat alteration at the shoreline, lake, or estuary scale for 
the identified high-priority species. 

Simple validated models will be developed to quantitatively predict changes in societally 
valuable populations due to areal loss (either partial or total) of a given habitat within the spatial 
mosaic of habitats that constitute an aquatic shoreline or an entire lake or estuarine system. This 
is the central focus of this research component. Development of single-species models to show 
changes to high-priority populations that result from areal habitat loss will be a worthwhile 
stand-alone goal for NHEERL. Once these single-species models have been developed, research 
can proceed to research step 3 (below), the development of multi-species models. 

The single-species models may be based on quantitative empirical measures of population 
density, production, or export in each habitat; or on quantitative estimates of fecundity and 
survival. As appropriate (when species life history dictates, or when broad regional coverage is 
needed), models also may be based on relative measures of population response. In each region, 
EPA researchers will ne^ to detemiine which approach is best suited to accomplishing the goals 
of this research, with an eye towards ultimate application to risk assessment models. 

As a first step in model development, researchers will consider the available literature, assemble 
existing models and data, and determine appropriate research approaches for each habitat and 


25 



population. Wherever possible, existing models and data will be used to develop these habitat 
alteration-population response models. When this is not possible, researchers will plan the 
research needed to develop the models. 

This work will be done from the aquatic shoreline scale (including intertidal and shallow habitats 
through deep water habitats) to the whole-lake/whole-estuary scale, as appropriate for the 
populations in question. The majority of lake research will probably take place in the U.S. Great 
Lakes, but smaller lakes are not excluded from consideration. In addition to developing these 
habitat alteration-population response models, targeted community-level and process-level work 
may be required to achieve the desired results. For example, it may be necessary to quantify key 
functions of certain habitats, or to examine how species in habitats of interest are linked to 
adjacent habitats. In each situation, proposed research will be designed to support habitat 
alteration-f)opulation response models. 

An important point is that these models need not consider the full life history of each population. 
The goal of this work is to provide the scientific basis for resolving enviroiunental protection 
policy and regulatory questions, not for policies to enhance fishable biomass, and so models may 
focus on specific stages of valuable species (e.g., juveniles). These "population models" may 
then actually model a sub-population, a single life history stage, or a specific "bottleneck" in a 
population. Otherwise, full-life-history models would need to consider harvest and over fishing, 
which is only going to be of incidental policy interest to EPA regulatory staff. The models we 
propose are designed to provide the necessary scientific basis to protect habitats by relating 
habitat characteristics to population or sub-population endpoints for fish, shellfish, and wildlife. 

Spatial habitat mapping also will be a component of this work. The consequences of habitat loss 
need to be considered in the context of how much of each key habitat is available in the area of 
interest Habitat mapping a shoreline, lake, estuary, or subestuary will be a vital tool in 
application of models. For this reason, spatial habitat mapping also should be a part of the 
quantitative research involved in creating these models. 

In some cases when developing these models, it will be possible to link the status of a certain 
population to alteration of a single (often vegetated) habitat This is most realistic for species 
that are very tightly tied at key life stages to single habitats that act as population "bottlenecks". 
This may apply to penaeid shrimp and vegetated marsh and seagrass habitats in the Gulf of 
Mexico, to bay scallops and seagrass habitat in the Mid-Atlantic and Northeast, and to other 
species and habitats. In these situations, project 1 will be a more appropriate means of 
determining population response. Project 1 evaluates in better detail at the single-habitat scale. 
Project 2 is intended to be fairly crude at the scale of the individual habitat, looking primarily at 
the effects of areal habitat loss on populations within a larger setting; the shoreline, lake, or 
estuary. Alternatively, project 2 analyses may look at how habitat mosaics and landscape 
patterns affect the identified populations. In order to best meet the goals of the Aquatic Stressors 
Framework, projects 1 and 2 will need to work closely together. Approaches, data, and results 
will be shared between the two groups to produce the best product and to eliminate duplication of 
effort. In most cases, results of the more detailed habitat alteration work conducted under project 
1 will be directly incorporated into the larger scale habitat alteration-population response models 
described here. 


26 


For some populations, a single-habitat approach as in project 1 above may not be the most 
appropriate model. In particular, many economically valuable fishes are mobile, migrating from 
habitat to habitat. The scale which affects these populations most is that of an entire aquatic 
shoreline or even an entire lake or estuary, all of which are composed of many interconnected 
habitats. The shoreline approach, which considers the entire nearshore mosaic of habitats from 
intertidal through shallow subtidal and into deeper habitats, may be the smallest scale that can be 
applied meaningfully to mobile, economically valuable finfishes. Many of these species recruit 
into nearshore habitats (marshes, seagrass beds, and mud and sand shallows) for their Juvenile 
development, then move into deeper habitats as they grow. Shallow waters, whether vegetated or 
not, provide juveniles with a significant refuge from predation by larger aquatic predators (Ruiz 
et al. 1993). Shallow waters are also very susceptible to habitat destruction. The general life 
history pattern of "shallow-to-deeper" is true for many finfishes and mobile shellfish. Research 
at the scale of a shoreline, estuary, or lake is appropriate for species with these life history 
patterns. Thus the two projects will work together to deliver habitat alteration-population 
response models for high priority species. 

Researchers also will need to consider temporal variability in constructing these models. Given 
the short time frame for results (~ 8 years) relative to the time frame for cyclical fluctuations of 
some aquatic populations, researchers will need to consult historical long-term data sets for the 
species of primary concern. Fortunately, the focus on commercially and recreationally important 
aquatic species increases the likelihood that long term abundance data will be available. 

In order to achieve larger goals, continuing collaboration and coordination among Divisions will 
strive towards the goal of establishing comparable and quantitative methodologies. These efforts ■ 
will also link closely to different Aquatic Stressors research implementation plans that are 
delivering habitat alteration as stressor-response endpoints. Examples are the Nutrients plan 
(Section 5) and collaboration with other EPA laboratories, and other Federal and State agencies 
also will be required to develop the best possible products. Another important aspect of model 
development will involve determination of the data quality required to produce models with 
adequate predictive power. Models will be designed to provide valid information EPA needs to 
meet its regulatory requirements. 

3. Develop comprehensive multi-species models to quantify population responses to habitat 
alteration at the shoreline, lake, or estuary scale. 

A series of validated habitat alteration-population response relationships for the individual high 
priority species at the shoreline-, lake-, or estuary-scale (as described above) is the first-order 
goal of these efforts. Development of these single-species models would be a valuable and 
sufficient contribution of this project. However, where feasible, this work will be taken a step 
ftirther. A subsequent goal of this work is, for each region, to develop comprehensive multi¬ 
species models that can quantify the effects of habitat alteration on the entire suite of 
economically valuable and charismatic populations within a shoreline, lake, or estuary. These 
comprehensive multi-species models will be constructed initially by combining the species 
models described above, taking care to ensure that the sum of these models represents the 
complete set of major species. 


27 




As a whole-system example, consider seagrass habitat. When seagrass habitat is lost, it is not 
replaced with a lifeless void, but more typically with macroalgal or unvegetated habitats. 

Seagrass loss may cause populations of some valuable species to decrease, but populations of 
other valuable species in the estuary may simply move over, or may even increase. The total 
impacts of loss of a single habitat to aquatic populations within a lake or estuary must be 
assessed first, with consideration of those habitats that will replace the lost habitat; and second, 
with consideration of the entire suite of economically valuable fish, shellfish, and wildlife that 
will be affected. If only a small number of the important species are considered, 

"comprehensive" habitat alteration models will be driven by the initial selection of species, and 
may not reflect the true effects of habitat alteration. EPA ultimately wants to evaluate the 
societal impacts of habitat alteration. Though ambitious, it is therefore a priority (where 
possible) to develop truly comprehensive multi-species models that can examine habitat 
alteration and accurately predict a large majority of the impacts to economically valuable and 
charismatic species. 

These synthesis-oriented comprehensive models might also consider emergent properties that 
develop from combinations and arrangements of habitats within a shoreline, lake, or estuary. For 
example, a diversity of habitats arranged as a patchwork may or may not support more fish, 
shellfish, and wildlife than would uninterrupted expanses of the same habitats, or certain 
combinations of habitats, such as marsh and adjacent SAV, may be of particular value to fish, 
shellfish, and wildlife. These models might further consider how certain sentinel species are 
linked to the health of the ecosystems upon which they depend. Development of comprehensive 
multi-species models will require a synthesis phase (and iteration) to assemble the individual 
habitat alteration-population response relationships. The result will be validated comprehensive 
models, based on quantitative data, that can predict the total consequences of areal habitat loss to 
the great majority of economically valuable and charismatic populations. These comprehensive 
models also should be designed to place a quantitative fish/shellfish/wildlife resource value on 
shorelines, lakes, and estuaries, based on spatial habitat mapping. This resource value should 
reflect aquatic populations, and need not be tied to monetary standards. Multi-species models 
should predict quantitative changes in aquatic resource value that would result from habitat 
alteration to any mapped shoreline, lake, or estuary. This will allow better assessment and 
management of aquatic habitats and resources. 

Both the individual models and the comprehensive multi-species models should be designed 
around application to a risk-assessment framework. This will allow insertion of the habitat 
models into larger spatially explicit risk assessment models that can consider multiple stressors 
including habitat alteration, toxic chemicals, and others. 

4. Develop classification schemes within each identified habitat or system type where other 
important factors (salinity, geomorphology, and tidal energy) will affect the ability of habitats or 
systems to support priority populations. 

A classification scheme is necessary to allow appropriate application of the single-species and 
multi-species habitat alteration-population response models described above. For example, 
juvenile summer flounder settle in shallow sandy or muddy mesohaline and polyhaline estuarine 
habitats in the Mid-Atlantic, but do not utilize oligohaline areas (Rogers and Van Den Avyle 


28 


1983). As support for summer flounder populations. Mid-Atlantic shallows should be classified 
by salinity. Other examples of classification may include lagoonal systems versus riverine 
systems versus embayments, and microtidal systems versus macrotidal systems. Regionalization 
will, of necessity, be part of this exercise as well. In some cases, these questions of habitat 
classification will be answerable through the literature; in other cases new research efforts may 
be required. 

Products 

APM lA FY02 Listings of the high-priority species of fish, shellfish, and aquatic-dependent 
wildlife for study in each biogeographic region, and listings of the habitats that are considered to 
be critical to each (WED). 

APM 3F (GPRA # 58) FY04 Preliminary report characterizing relationships between abundance, 
quality, and arrangement of various habitat types and selected biotic assessment endpoints in 
coastal systems (WED). 

APM 5B FY06 Report characterizing relationships between abundance, quality, and arrangement 
of various habitat types and selected biotic assessment endpoints in coastal systems (WED, AED, 
GED, MED). 

APM 6C FY08 Synthesized quantitative species-habitat relationships suitable for developing 
regional habitat-based biocriteria for shorelines, lakes, and estuaries (AED, GED, MED, WED). 

Benefits of Products 

This work is designed to provide EPA Program Offices and Regions with simple validated 
models that can quantify the effects of alteration or loss (either partial or total) of any major lake 
or estuarine habitat on populations of economically valuable or charismatic fish, shellfish, and 
wildlife. This work will also be of value to other Federal, State, and local managers striving to 
protect living aquatic resources from degradation due to habitat alteration. 

This work will also link to the load-response efforts proposed in the Nutrients research 
implementation plan (Section 5), in that habitat alteration can occur through destruction and 
fragmentation, nutrient loadings, or other stressors. The products in the Nutrient plan (Section 5) 
regarding load-response models include loss of SAV habitat, loss of benthic habitat due to 
hypoxia and anoxia, and increases in macroalgal habitat; but do not focus on fish, shellfish, and 
wildlife. The aquatic shoreline, whole-estuary, and whole-lake scale habitat alteration- 
jX)pulation response work proposed here can tie the nutrient habitat endpoints to population 
endpoints for economically valuable or charismatic species. 

Since these habitat alteration-population response models will be designed to fit within a risk- 
assessment framework by quantitatively linking habitat alteration to population response, this 
work can also integrate into larger, multi-scalar, spatially explicit, multiple stressor risk 
assessment models. 


29 


Project Title 3, Salmon and Native Fish Habitat Research 
Project Coordination and Resources (9.4 FTEs: MED-1.3, WED-8.1). 

Introduction 

The research described in this project deals with the influence of human activities on aquatic and 
aquatic-dependent biota at landscape, watershed, and regional scales. Specifically, it will 
examine watershed and landscape scale habitat issues affecting salmon and native fishes in the 
Pacific Northwest, and fishes reliant upon Great Lake coastal wetlands. 

Objectives 

• To evaluate and to quantify the influence of human activities at the landscape and 
watershed scales on native fish habitat and fish populations, including wild Pacific 
salmon and economically and ecologically important Great Lakes fishes. 

• To evaluate how habitat spatial structure and connectivity of habitat in stream networks, 
wetlands, lakes, and estuaries influence native fishes, including wild Pacific salmon and 
wetland-dependent fish populations and overall biodiversity. 

Scientific Approach 

Although many aspects of aquatic habitat-fish population relationships have been studied, many 
knowledge gaps exist. Relatively little attention has been focused on the relationships between 
landscape structure and fish assemblages, and landscape structure and aquatic habitat. 

Population declines of salmon and other native fish accentuate the need for the quantification of 
these landscape relationships. In the report. From the Edge: Science to Support Restoration of 
Pacific Salmon^ the Committee on Environment and Natural Resources (CENR) identified 
science needs for Pacific salmon and related species (CENR 2000). CENR indicated that habitat 
for salmonids and all native aquatic species, and hence their populations, are strongly influenced 
by watershed conditions at a landscape scale. Modeling and decision support tools are required 
to incorporate land use change relative to habitat on the extensive spatial scale, and must 
incorporate temporal changes (habitats are dynamic). 

The research will be conducted in two regions, the Great Lakes and the Pacific Northwest. In the 
Great Lakes, research will focus on coastal wetland fish assemblages. There are approximately 
200 species of fish in the Great Lakes. It is estimated that about 90% of those species are directly 
dependent on coastal wetlands for some aspect of their life history. Among those species that are 
heavily dependent on coastal wetlands are yellow perch, northern pike, largemouth bass, walleye, 
and a number of forage fishes (Jude and Pampas 1992, Brazner 1997). All of these populations 
also have relatively important commercial and/or sport fisheries throughout the Great Lakes and 
all appear to be in decline. Habitat alteration is thought to be the most important contributor to 
these declines, but over-fishing, pollutants, and exotics are also considered important threats 
(Whillans 1992). In the Pacific Northwest, research will focus on wild Pacific salmon and native 
fish. Many of the anadromous salmonids populations in the Pacific Northwest are in serious 


30 


decline, and numerous populations are now listed under ESA. Landscape change, water 
pollution, introduced predators, fishing, hydro power development, disadvantageous ocean 
conditions, and other factors have led to the extinction or decline of many stocks (Bauer and 
Ralph 1999, CENR 2000). Research will be developed to allow for comparisons of factors 
influencing native fish assemblages in the two regions. 

Upland and Riparian Effects on In-stream and Coastal Wetland Condition 

In the Pacific Northwest, these efforts will be based on an integrated modeling/field study 
approach. An existing model developed by NMFS (1992) simulates coho salmon population 
dynamics based on in-stream habitat condition. For this model, in-stream habitat condition was 
determined through simple stream reach classification that does not reflect watershed land 
use/land cover conditions. If, however, we are to be able to examine how upland management 
affects fish dynamics, then it is necessary to understand how in-stream habitat condition is 
influenced by the surrounding uplands and riparian areas. Shading by riparian trees, woody 
debris supply, non-point source introduction of sediments and nutrients, and landslides are all 
examples of important upland processes that can affect in-stream habitat condition and which 
could be influenced by upland management actions. Such information also allows us to predict 
habitat condition, based on upland characteristics, at locations which have not been sampled. 
Besides affecting habitat condition, upland factors can also influence fish mobility. For example, 
warm water temperatures or landslides could reduce or completely prevent fish movement 
between stream reaches. Another important upland/riparian issue associated with the restoration 
of Pacific salmon is the possible need for nutrient additions (i.e., raw or processed salmon 
carcasses, and commercially produced organic or inorganic fertilizers) to headwaters (e.g., 
watersheds, lakes, or streams) to compensate for the loss of marine derived nutrients previously 
supplied by healthy salmon populations. Determining the ecological effects of surrounding 
upland areas on in-stream condition is therefore a critical component of our research. 

Technical approaches to examining upland effects on in-stream condition could include field 
studies, empirical modeling, and process modeling. Empirical modeling approaches would 
develop correlations between upland independent variables and in-stream response variables. 
Upland variables could be derived from GIS DATANET, and could be used to represent the 
watershed as a whole or the riparian zone in particular. Data for explanatory and response 
variables could be obtained through field sampling, other research projects (e.g.. Environmental 
Monitoring and Assessment Program, EMAP) or agencies, or through the literature. Process 
models would relate upland factors to in-stream condition based on specific processes. Examples 
include a model that predicts in-stream suspended sediment concentraticm based on soil 
characteristics, slope, upstream load, or a physical model that calculates water temperature based 
on shading by trees. Other modeling approaches are also available. We envision linking such 
models with a salmon population model to be able to examine the influence of land use changes 
on salmon and fish populations. 

In the Great Lakes, initial efforts to understand landscape influences on coastal wetland habitat 
condition and native fishes will be based on field studies designed to help build quantitative 
empirical models that can eventually be used to construct more process-oriented models. The 
current knowledge base related to watershed fragmentation effects in Great Lakes coastal 


31 


wetlands is quite limited. Although watershed fragmentation has been shown to be related to the 
structure and function of biotic assemblages in streams from Western Lake Superior (Detenbeck 
et al. 2000), fragmentation effects on habitat condition and biota associated with Great Lakes 
coastal wetlands have not been well documented. We suspect that this sort of fragmentation- 
induced habitat alteration also will cause changes in higher tropic levels in Great Lakes coastal 
wetlands. 

Some of our endpoints for testing this idea may be yellow perch and northern pike population 
abundances, and overall fish biodiversity at river-influenced coastal wetlands having differences 
in fragmentation levels in their watersheds. GIS characterization, statistical analysis, and model 
development sequences will parallel those planned for stream watersheds in the Pacific 
Northwest. We plan to refine our definition of fragmentation to incorporate a variety of land-use 
types by using a "land-use equivalency** approach which will allow us to place our wetland sites 
along a vulnerability gradioit, and provide a better opportunity to link watershed land-use to 
habitat and fish response curves for Great Lakes coastal wetlands. Collaborations with 
researchers from other institutions will allow us to increase the number of sites where fish data 
will be available and expand the vulnerability gradient to include much of the Great Lakes that 
would not otherwise have been possible. 

Because we think the response to watershed fragmentation by wetland fishes will vary with 
wetland type, we will need to test whether a wetland classification system effectively groups 
coastal wetlands into similar response classes. Although there is a presumption that coastal 
wetland hydro geomorphology influences biota, there is little direct supporting evidence. It is 
well known, however, that aquatic community structure of higher tropic levels is influenced by 
vegetation structure in coastal wetlands, and vegetation stmcture appears to be related to 
hydrogeomorphology. So, it seems likely that our fish response variables (population and 
assemblage level) also will be related to differences in wetland hydrogeomorphology. After 
assessing whether this is the case at different coastal wetland types (e.g., open estuary, barred 
estuary, barrier beach lagoons, open coastal), we will be better able to extrapolate the 
significance of our fragmentation results on a region-wide basis by knowing the distribution of 
wetland types across the landscape and the fragmentation levels in their watersheds. 

Effects of Network Structure and Connectivity on Fish Movement 

Because fish are mobile, they are not limited to nor exclusively influenced by the habitat quality 
of a single stream reach. Rather, they move between reaches and may require different habitat 
conditions during different life stages. The spatial distribution of habitat condition and the 
ability of fish to move between reaches are therefore important considerations. For example, 
salmonids returning from the ocean attempt to reach the same stream reach in which they were 
spawned. Any obstruction in the stream network, which forces them to expend more energy to 
return, could affect spawning success. If a barrier completely prevented them from returning to a 
particular home reach, then the ability of strays to decolonize new habitat would depend on the 
spatial distribution of habitat near the home reach and the occurrence of other obstructions to 
movement Thus any effort aimed at examining watershed management effects on fish 
populations needs to consider the effect of the watershed on the spatial structure of the network 
(e.g., the distribution of habitat condition) and on the level of connectivity among stream reaches. 


32 


This work will primarily be conducted in the Pacific Northwest, but the hydrogeomorphic 
classification being tested in Great Lakes coastal wetlands is based on hydrologic connectivity of 
wetlands to adjacent lake habitats and their watersheds, so many of the same habitat connectivity 
issues are relevant to the Great Lakes studies and will be implicitly incorporated into their design. 

In the Pacific Northwest, the approach to examining the effects of network structure and 
connectivity will be to build a spatially explicit network data structure that includes habitat 
quality and connectivity attributes and which can be linked to specific biological resptxise 
models. Such a network structure could be used in a number of ways. For example, it might be 
desirable to conduct simulations of several specific drainage networks, and to compare results 
between basins with high habitat quality and low habitat quality. Alternatively, it might be 
desirable to examine the effect of certain watershed characteristics (e.g., slope, catchment area, 
stream doisity) on fish dynamics by systematically varying those characteristics using synthetic 
landscapes. 

Biological Response of Fish to Habitat and Stream Network 

EPA has responsibilities under the CWA to restore and maintain the biological integrity of the 
nation’s waters. Therefore, it is desirable to understand how activities aimed at managing 
salmon would affect other fish species, in particular, native fish. To address these needs, field 
research and modeling efforts will be developed to examine how management actions would 
affect dynamics of various fish groups. This may include modeling at different levels of 
organization. First, species-level models would examine the biological response of particular 
species to watershed and network structure. Models would be run separately for salmon and 
possibly a few other species representative of different life history strategies. This will allow us 
to examine how salmon and fish with different habitat needs respond to a common set of 
management actions. Second, the biological response modeling could also include exploratory 
assemblage-level modeling. In this case the dynamic behavior being tracked is overall species 
richness, rather than abundance of a particular species. This allows us to examine community- 
level response to management actions. 

Products 

APM 2A FY03 Prototype watershed-stream network modeling approach for Pacific salmon 
(WED). 

APM 3C FY04 Report characterizing the relationship between habitat in stream networks and 
salmon-native fish for coastal Oregon watersheds (WED). 

APM 6A FY04 Ecological consequences of marine derived nutrients and nutrient enrichment for 
aquatic biota and stream habitat quality, with an emphasis on salmon and native fish (WED). 

APM 4A FY05 Develop indices of watershed integrity based on land use/land cover and 
relationships to fish (WED, MED). 


33 


APM 5A FY05 Reports characterizing the relationship between landscape-scale habitat mosaics 
and native fish by wetland type in the Great Lakes (MED). 

APM 6B FY07 Regional models of landscape influence of salmon/native fish in the Pacific 
Northwest and native fish in Great Lake coastal wetlands (WED, MED). 

APM 6D FY08 Interactions between stream nutrients and habitat alteration on water quality and 
aquatic life (WED). 

Benefits of Products 

Research will allow explicit evaluation of human activities at landscape and watershed scales on 
salmon and native fish. This will be of direct benefit to OW, EPA Regions, and an interagency 
effort on salmon restoration. 

Project Title 4, Multiple Stressor Risks to Common Loon and Other Piscivorous Bird 
Populations (cross-listed in Section 7, Toxic Chemicals^ Project B3) 

Project Coordination and Resources (1.5 FTEs) 

AED-1.5, and 6.9 additional FTEs devoted to mercury-loon research described in Section 
7,Toxic Chemicals, project B3. 

Introduction 

This project examines the interactive effects of multiple stressors, including landscape-level 
habitat alteration and mercury, on common loons and other piscivorous bird populations (‘loon 
project’). This project was developed as a case study implementing NHEERL’s WRS (EPA 
2000), and demonstrating an integrated approach to large scale, population-landscape-stressor 
assessments. There are significant habitat components to this project, including evaluating the 
spatial configuration of loon habitat and mercury impacts in the landscape mosaic and the issue 
of scaling up from local to regional impact assessments. Because habitat and toxic chemicals 
issues are integrally linked within the demonstration project, the project is relevant to issues 
within this section, as well those relative to toxic chemicals (Section 7). Therefore, a brief 
description of those elements of the loon project related to the assessment of risks of habitat 
alterations at multiple geographic scales, appears here as project 4. To avoid redundancy, a 
complete description of the project appears in Section 7, project B3. 

Two key research areas, defined within the WRS and described below, reflect the need to 
consider landscape context and scale in order to achieve the scientific and management goals of 
the risk assessment. 

1. Research to Advance Techniques for Assessing the Relative Risk of Chemical and Non¬ 
chemical Stressors on Wildlife Populations. 


34 


Landscape characterization studies, combined with experimental approaches, are required to 
better quantify the relative impacts of chemical stressors, habitat alterations, and the introduction 
of exotic species on wildlife populations. Associated with this effort is the need to develop and 
integrate predictive models so that the outcome of different management scenarios, based on 
chemical loading, habitat alterations, exotic species control, and other management options, can 
be quantified. 

2. Research to Define Appropriate Geographical Regions/Spatial Scales for Wildlife Risk 
Assessments. 

A significant effort is needed to define scientifically credible spatial scales for wildlife risk 
assessments. Habitat requirements for wildlife species associated with aquatic and terrestrial 
ecosystems must be established and referenced to regulatory jurisdictions to ensure coordinated 
implementation of risk-based decisions. A consensus on current or potential habitat ranges are 
needed to identify wildlife species of concern and to evaluate approaches in risk assessments that 
consider spatial population structure (EPA 2000). 

Within the WRS, three major research objectives have been defined to address these needs. The 
third objective defines the focus for the habitat alteration component of the loon project, i.e., the 
development of approaches for evaluating relative risks from chemical and non-chemical 
stressors on spatially structured wildlife populations across large areas or regions ('^geospatial 
modeling"). Research described in the loon project will address issues associated with the spatial 
and temporal heterogeneity of populations and stressors in real landscapes. This landscape 
context provides a basis for understanding and quantifying how spatio-temporally varying 
stressors influence the distribution of wildlife populations. Thus, the approaches, models, and 
methods developed within this project are designed both to assess risks from multiple stressors 
and evaluate the relative effectiveness of alternative management strategies. 

Objectives 

Specific to the goals for habitat alteration research, the relevant research objective can be 
described as the development of approaches for evaluating the relative risks from chemical and 
non-chemical stressors on spatially-structured wildlife populations across large areas or regions. 
Consistent with this objective and to address the WRS objectives described above, research 
activities within the loon demonstration project focus on the development of geospatial modeling 
methods to assess the relative impact of heterogeneously distributed stressors, including dietary 
methylmercury, habitat degradation, acidification, and human disturbance on populations of the 
common loon, which is resident to the northeastern portion of the U.S. and Canada. For this 
purpose, research activities will include the develop of methods to identify spatial relationships 
among stressors (i.e., correlations in distributions), potential interactions among stressors, and the 
relative risks among potential stressors to populations of loons at varying spatial scales. 

Scientific Approach 

Consistent with the approach described generally for habitat alteration research, there are two 
distinct research components for habitat research within the loon project. The first of these 


35 


addresses the need to describe the relationship between habitat and biota (’’habitat/suitability 
models") at appropriate spatial scales and with sufficient detail and resolution to quantify the 
effects of both incremental and catastrophic habitat alteration. The second step involves 
integrating habitat alteration effects with those of other stressors ("integration within a spatial 
context"), so that resource protection and restoration priorities can be evaluated at spatial scales 
up to and including regions or large receiving bodies. 

For this project, the focal species was selected partly to take advantage of existing data that may 
permit the development of linkages between habitat and biological fitness. Specifically, fine- 
scaled spatially-referenced information on presence and condition of individual loons across 
large geographic areas is available through long-term loon monitoring programs that exist in the 
upper Midwest and the Northeast. In addition to information from these programs, available 
monitoring databases and/or aerial photographs, provide information to characterize habitat 
quality. For loons, key habitat characteristics may include the presence of suitable nesting and 
brood rearing sites, measures of human disturbance, density or extent of human dwellings and 
other activities around lakes, turbidity, and availability of suitable forage fish supplies. This 
unique set of spatially-referenced data will permit the development of habitat suitability 
approaches and models, relating environment factors and biological condition. 

Within the loon project, integration within a spatial context has been approached through the 
development and application of spatially-explicit population models that incorporate stressor- 
response relationships that will be applied within the spatially-diverse landscape. Specifically, 
within this project, spatial models will be used to evaluate how loon life history, spatial 
heterogeneity, and interactions among stressors in the landscape drive the relationship among 
breeding success on individual lakes and population trends across broad regions. These models 
will be used to: 1) define what constitutes a population (within the context of the assessment 
question) and how sub-populations interact in a heterogeneous landscape, 2) determine the 
appropriate spatial scale for assessment questions, and 3) determine the relative risks presented 
by different stressors. This model development would be a primary objective of this 
demonstration project. 

Products 

As defined within this plan, these specific products will be developed: 

APM 2B FY04 Habitat suitability indices to support population models for projecting relative 
risks of multiple stressors including toxic chemicals and habitat alteration to common loons 
(AED). 

Also see Section 7 Toxic Chemicals, Project B3 for associated products. 

Benefits of Research 

This research will allow explicit evaluation of multiple stressors on piscivorous wildlife and lead 
to the development of risk-based criteria. This will be of direct benefit to Program Offices, 
Regions, and interagency efforts to protect important wildlife species. More broadly, this 


36 


research will permit an evolution towards a landscape assessment approach for examining critical 
environmental problems over larger spatial scales and the assessment of the cumulative risk 
resulting from multiple stressors. This approach alFows for a more comprehensive perspective 
for evaluating the condition of communities, watersheds and ecoregions. A landscape 
assessment approach also provides the ability to evaluate the status and trends of a variety of 
ecological resources at multiple scales so that relationships of stressors and effects can be 
developed to establish conditions which are influencing the impacts on wildlife populations. 

Gap Analysis 

A. The following research is within the scope of this plan but outside NHEERL’s current 
manpower, expertise, or sampling capability: 

• Assessment data required to extrapolate habitat-biota relationships studied by ORD to the 
population of all systems for which nutrient, sediment, and biocrheria are required will 
necessitate collaboration with other Federal and State agencies, non-governmental 
organizations, and academic institutions. 

• Near shore fish sampling in Great Lakes (e.g., abundance of commercially and 
recreationally valuable species) to support multiple habitat research will require 
collaboration with entities capable of open-water fish sampling. 

• Development of habitat alteration-population response models for the southern Atlantic 
coast (e.g., Carolinian biogeographic province), the southern Pacific coast (e.g., San 
Diego biogeographic jx'ovince), and Puerto Rico and the U.S. Virgin Islands, especially 
for populations of commercially valuable fish and shellfish. 

• Survey of songbird, hawk, and waterfowl utilization of reference coastal wetlands of high 
ecological integrity, and of disturbed wetlands of varying anthropogenic alteration are 
currently outside NHEERL expertise and/or manpower. Wetlands of interest for the 
surveys are all coastal vegetated habitats such as fresh- and salt-water marshes, SAV, and 
emergent aquatic vegetation. 

• Development of quantitative methods to evaluate the restoration success of the structure 
and function of habitats that support populations of commercially valuable fish, shellfish, 
and wildlife. 

B. The following research is outside the scope of this plan (which focuses primarily on fish, 
shellfish, and wildlife population endpoints of concern to society), but relates to other ecological 
endpoints that may be also relevant to society: 

• Understanding of the effects of habitat alteration on fish and other biotic assemblages 
(e.g., zoobenthos, macroinvertebrates) from both a structural (including biodiversity) and 
functional perspective even where these responses cannot be immediately linked to fish, 
shellfish, and wildlife populations of interest 


37 



• Understanding of the effects of habitat alteration (both within watersheds and their 
coastal ecosystems) on the exchange of materials (e.g., dissolved nutrients, particulate 
organic matter, organisms) with adjacent waters where these responses cannot be 
immediately linked to fish, shellfish, and wildlife populations of interest. 

• Role of areas of critical habitats along the coast in providing flood and erosion control, 
and an understanding of the effects of habitat alteration (both within watersheds and their 
coastal ecosystems) on the retention of sediments by coastal ecosystems. 

• Importance of areas of critical habitats in watersheds adjacent to coastal waters in 
providing nutrient filtration (e.g., riparian zones, inland wetlands, forests, salt marshes). 

• Effect of nonindigenous organisms on the structure and function of wetlands and other 
critical habitats. 

• Mechanistic understanding of nutrient effects on vegetation and tropic structure of critical 
habitats. 

References 

Bauer, S.B., Ralph, S.C. 1999. Aquatic habitat indicators and their application to water quality 
objectives within the Clean Water Act. EPA 910-R-99-014. EPA, Region 10. 

Brazner, J.C. 1997. Regional, habitat, and human development influences on coastal wetland 
and beach fish assemblages in Green Bay, Lake Michigan. J. Great Lakes Res. 23:36-51. 

CENR. 2000. From the edge: science to support restoration of Pacific salmon. National Science 
and Technology Council. 

Detenbeck, N.E., Arthur, J.W., Bertelsen, S.L., Brazner, J.C., Snarski, V.M., Taylor, D.L., 
Thompson, J A. 2000. Western Lake Superior comparative watershed study. 

Environ. Toxicol Chem. 19:1174-1181. 

EPA. 1990. Reducing risk: setting priorities and strategies for environmental protection. 
SAB-EC-90-021.26 pp. 

EPA. 1999. EPA’s framework for community-based environmental protection. EPA 237-K-99- 
001. Washington, DC. 40 pp. 

EPA. 2000. Wildlife Research strategy. EPA. NHEERL/ORD. September. 

Houde, E.D., Rutherford, E.S. 1993. Recent trends in estuarine fisheries: predictions of fish 
production and yield. Estuaries 16:161 -176. 

Jude, D.J., Pampas, J. 1992. Fish utilization of Great Lakes coastal wetlands. J. Great Lakes 
Res. 18:651-672. 


38 


NMFS. 1992. Quantifying resource loss through habitat degradation: proceedings of the first 
NMFS Northeast environmental workshop, March 13-14 1991. NMFS-F/NER-3. Northeast 
Regional Operations Office, Gloucester, MA. 137 pp. 

NRC. 1997. Building a Foundation for Sound Environmental Decisions. National Academy 
Press, Washington, DC. 87 pp. 

Rapport, D.J., Costanze, R., Epstein, P.R., Gaudet, C.L., Levins, R. 1998. Ecosystem Health. 
Blackwell Science, Malden, MA. 372 pp. 

Rogers, S.G., Van Den Avyle, M.J. 1983. Species profiles: life histories and 
environmental requirements for coastal fishes and invertebrates (South Atlantic) -- summer 
flounder. FWS/OBS-82/11.15. U.S. Fish and Wildlife Service, U.S. Army Corps of Engineers, 
TR EL-82-4. 14 pp. 

Ruiz, G.M., Hines, A.H., Posey, M.H. 1993. Shallow water as a refuge habitat for fish and 
crustaceans in non-vegetated estuaries: an example from Chesapeake Bay. Mar. Ecol 
Prog. Ser. 99:1-16. 

Stein, B.A., Flack, S.R. 1997. 1997 species report card: the state of U.S. plants and animals. The 
Nature Conservancy, Arlington, VA. 

Warren, M.L., Burr, B.M. 1994. Status of freshwater fishes of the United States: overview of an 
imperiled fauna. Fisheries 19:6-17. 

Whillans, T.H. 1992. Assessing threats to fishery values of Great Lakes wetlands. In Kusler, J., 
Smardon, R., eds.. Wetlands of the Great Lakes: Protection, Restoration, Policies and Status of 
the Science^ Proceedings of the International Wetland Symposium, May 16-19, 1990, Niagara 
Falls, NY, pp. 156-165. 


39 



Section 5. 

Implementation Plan for Nutrients Research 


Problem 

There is growing evidence that human activities have dramatically changed the amounts, 
distribution, and movement of major nutrient elements (nitrogen-N and phosphorus-P) in the 
landscape and have increased nutrient loading to receiving waters. Some of these changes affect 
use of the nation’s aquatic resources, and pose risks to human health and the environment (NRC 
2000). EPA is in the process of developing guidelines that States and Tribes must use to set 
nutrient criteria for our nation’s waters. For waters failing to meet WQS, States and Tribes will 
be required to develop TMDLs to eliminate the causes of non-attainment. Our current level of 
understanding of aquatic ecosystem function is inadequate to allow us to extrapolate knowledge 
of nutrient loading relationships for systems for wdiich we have intensive data to accurately 
predict adverse effects on specific systems with more limited data. NHEERL research will 
provide the scientific information on load-response relationships that are required to develop 
numeric nutrient criteria protective of aquatic life. 

This research implementation plan is ambitious. A complete understanding of the effects of 
nutrients on aquatic ecosystems will require additional research. The projects listed in this plan 
outline the objectives needed to establish the scientific basis for WQC and TMDLs associated 
with nutrients in coastal systems (coastal wetlands, embayments, estuaries, and near coastal 
waters). The decision to focus on coastal waters is based on the complexities of these systems 
and OW’s prioritization of research needs by waterbody type. The most important response 
categories for study are given below: 

Increase in algal production (or carbon supply as defined by Nixon 1995) and/or changes in algal 
community composition, which can result in harmful algal blooms (HABs), are principal 
causative agents for the three following effects: 

1. Low dissolved oxygen (DO) or hypoxia leading to fish kills or loss of shellfish and 
degradation of benthic habitats; 

2. Loss of natural SA V habitat^ important to fish and other biota and due to changes in water 
clarity, epiphytic growth, or smothering by invasive algae; 

3. Shifts in basic food webs leading to loss of commercially important fisheries and overall 
aquatic biodiversity. 

The following pathways define our current understanding of how nutrients affect each of these 
endpoints: 


40 


Low DO 


Increased nutrient loading to an estuary/receiving water stimulates primary production (largely, 
though not exclusively, phytoplanktonic) and produces excess carbon biomass that sinks to the 
bottom waters/benthos resulting in respiratory oxygen demand that may exceed oxygen supply. 
For this endpoint, our objective is to define relationships between nutrient load and factors 
affecting DO concentration in sensitive portions of receiving water bodies. As part of this effort, 
NHEERL is developing DO requirements to protect indigenous species in various coastal regions 
(North and South Atlantic, Gulf of Mexico, and Pacific). Criteria have been developed for the 
Atlantic coastline between Cape Cod and Cape Hatteras (EPA 2000). Ultimately, this research 
will provide relationships between nutrient inputs and DO concentrations, which will protect 
indigenous species in various coastal regions. 

SA V Loss 

Increased nutrient loading can result in an accumulation of phytoplankton, epiphytic, and 
macroalgal biomass/carbon that shades SAV or alters sediment geochemistry and results in loss 
of areal coverage. For this endpoint, our objectives are to develop, for the nation's coastal 
receiving waters, sufficient understanding of the relationship between SAV loss and nutrient 
loading to provide a sound scientific basis for establishment of nutrient criteria that would protect 
these important habitats from degradation or loss and aid in restoration efforts. This work will 
directly support or interact with the Habitat Alteration (Section 4) and Diagnostics (Section 8) 
research implemoitation plans. 

Shifts in Food Webs 

Changing nutrient loadings (includes increased loading, changes in loading ratios, and changes in 
the mode/timing of delivery) alter species composition of primary producers. Effects of this shift 
are transmitted through the food web, altering the consumer-food web dynamics (carbon or 
energy flow pathway) in receiving waters. The result is a change in primary producers that does 
not support existing food webs (and hence alters the biological integrity of ecosystems) and does 
not sup|X)rt commercially important fish and shellfish production. The objective of food web 
research is to identify nutrient loading thresholds that qause shifts in primary producers and other 
key components of the food web. In addition, we will assess the use of food web structure and 
processes to improve our ability to classify systems and to predict differences in response to 
nutrients that affect hypoxia and SAV. 

This research will require developing classification schemes for each of these endpoints so that 
aquatic systems can be grouped according to their expected responses to nutrient loading, to aid 
in the process of setting nutrient criteria and TMDLs. NRC (2000) recommends that 
classification frameworks be developed that can be generalized to a broader range of features and 
processes than the current classification schemes, which are focused on individual features (i.e., 
flushing or light). NHEERL’s classification efforts will focus on coastal receiving waters 
(including estuaries, near-coastal waters and the Great Lakes). Our classification efforts will 
focus on understanding and linking the influence of physical, chemical, and biological factors to 
the response to nutrients across the Nation’s coastal receiving waters. 


41 



NHEERL nutrient research efforts will provide nutrient-response relationships for diagnostic 
efforts (Section 8) and, once additional critical habitats subject to effects of nutrients are 
identified by habitat alteration research (Section 4), we will develop plans for nutrient load- 
response research in those critical habitats. To maximize our efforts, we also must link our 
efforts with other Laboratories in EPA (e.g., NERL), other Federal agencies (e.g., U.S. 

Geological Survey [USGS]), NOAA, and academic institutions collecting data on coastal 
systems. We have initiated efforts to provide common data and files across NHEERL’s 
Divisions and will continue to seek collaborations to strengthen our efforts. 

Goals 

The primary goal of this research is to provide the scientific basis and load-response relationships 
that are required to develop numeric nutrient criteria protective of aquatic life. The focus of this 
research is on coastal receiving waters and does not apply directly to the understanding of load- 
response relationships for other water bodies (e.g., streams, rivers, lakes, and wetlands). 
Therefore, NHEERL research will initially define and quantify relationships between nutrient 
loading and ecological responses for coastal aquatic resources. The APGs and associated APMs 
for this research are listed below. Note: some APGs and APMs (including those under GPRA) 
were established before this document was written. They are listed here along with APGs and 
APMs that were developed under the aquatic stressors process. 

APG 1 FY02 (GPRA # 030) Provide a strategic approach for developing TMDL-driven 
thresholds for protecting ecosystems from nutrients and sedimentation. 

APM lA FY02 (GPRA # 163) Generalized seagrass/rhizosphere model capable of 
predicting effects of reduced light, sedimentation, nutrient depletion, and toxic effects of 
sulfides (WED). 

APM IB FY02 (GPRA # 164) Effects on estuarine submerged aquatic vegetation from 
changes in light quantity and quality due to increased levels of suspended solids (GED). 

APM 1C FY02 (GPRA # 165) Minimum dissolved oxygen requiremoits of aquatic 
animals in the Gulf of Mexico estuaries as a measure of the effect of nutrient enrichment 
(GED). 

APM ID FY 02 (GPRA # 166) Effects of nutrient loadings and altered nutrient ratios on 
HABs (GED). 

APG 2 FY03 Provide the science to support consistent dissolved oxygen criteria for prevention 
of hypoxia impacts in all coastal regions of the US. 

APM 2A FY03 Minimum DO requirements for a suite of the important marine organisms 
(fish and crustaceans) from the Atlantic, Pacific, and Gulf of Mexico coastal waters of the 
U.S. (AED). 


42 


APG 3 FY03 (GPRA #15) Complete the framework for including dissolved oxygen and other 
receiving water thresholds into watershed management for nutrients. 

APM 3 A (GPRA #201) Comparison of effects of zooplankton grazing on estuarine 
phytoplankton community under differing natural levels of grazing (GED). 

APG 4 FY04 Provide first generation protocol to classify eutrophication models for nutrient load 
allocation in coastal systems. 

APM 4A FY03 Propose classification scheme for predicting sensitivity of coastal 
receiving waters to effects of nutrients on DO (MED, AED, GED). 

APG 5 FY07 Provide the scientific foundation for establishing site-specific nutrient threshold 
criteria to protect estuarine SAV. 

APM 5A FY02 Report on structural and functional characteristics of SAV rhizospheric 
communities (GED). 

APM 5B FY03 Correlation of water quality with SAV change (GED). 

APM 5C FY04 Report on environmental requirements of three main species of seagrasses 
(WED). 

APM 5D FY04 Development of stress-response model iorZostera marina in Pacific 
Northwest and validation of stress-response model for Thalassia testudinum (WED). 

APM 5E FY04 Development of empirical load-response models iox7x)stera marina in 
NE U.S. (AED). 

APM 5F FY05 Development of load-response models for estuaries of Pacific Northwest 
and Gulf Coast, and validation of stress-response model for Zostera marina in NE U.S. 
(WED, GED, AED). 

APM 5G FY05 Propose classification scheme for predicting sensitivity of coastal 
receiving waters to the effects of nutrients on SAV (WED, GED, AED). 

APM 5H FY06 Report on the empirical and numeric models for SAV (WED, GED, 
AED). 

APM 51 FY07 Report on a classification scheme for grouping coastal receiving waters 
based on sensitivity to nutrients (WED, GED, AED). 

APG 6 FY07 Provide scientific foundation for development and application of quantitative 
measures of food web attributes that are sensitive to ecological changes associated with nutrient 
enrichment. 


43 


APM 6A FY04 Sensitivity of food web responses to nutrient loading in coastal systems 
(GED). 

APM 6B FY05 Propose classification scheme for predicting sensitivity of coastal 
receiving waters to effects of nutrients on food web structure (GED). 

APM 6C FY06 Report on empirical and numeric models for food webs (WED). 

APM 6D FY 06 Report on parameterization of food web models (GED). 

APM 6E FY06 Report on classification scheme for grouping coastal or lake receiving 
waters based on sensitivity to food web alterations (WED, GED, AED, MED). 

Critical Path 

The components of the critical path seen in Figure 5 consist of five main steps that are the same 
or each of the response endpoints (DO, SAV, Food Webs): 

Step L Mine and Assess Existing Information. 

Evaluate available data and models from the peer-reviewed literature and determine if the data 
and models are useful to aid the development and improvement of nutrient load-response models. 
This will be a continuous process since new data and models are continuously being developed 
by other Federal and academic institutions. 

Step 2, Develop Conceptual Models, 

Conceptual models, describing how the three assessment endpoints respond to excess nutrients, 
will be defined in order to catalog the controlling mechanisms and processes. 

Step 3, Develop Classification Scheme, 

Included within this step is the development of a classification scheme and the assembly of data 
needed to apply a classification scheme across the Atlantic, Gulf, Pacific, and Great Lakes 
receiving waters. A tabulation of some of the factors that likely will be used in classifying 
receiving waters or in scaling or standardizing loading or response variables can be found in 
Table 3. 


44 


Database of existing information 


,_i_, 

Conceptual models for nutrient load-endpoint responses 

I _ 

Classification scheme for receiving water bodies/endpoints 

I 

Standard or comparable methods for assessment endpoints 

I 

Nutrient load - water body response models 


Figure 5. Critical path for research on the development of nutrient response 
relationships for coastal receiving waters. 

Step 4. Develop Standard Methods and Procedures. 

This task will involve the determination of a set of standard measurement endpoints for the 
assessment endpoints. Comparability of measurements and data across Regions/Ecological 
Divisions, where possible, will maximize utility of data collection, research, and modeling 
efforts. A periodic review of these endpoints and procedures across the Ecological Divisions will 
ensure consistency and comparability and maintain focus on our core objectives. 

Step 5. Develop Nutrient Loading-Ecological Response Models. 

Nutrient Inputs/Loading 

In order to develop nutrient loading-response relationships for each of the three endpoints, it is 
necessary to estimate nutrient loadings. There are at least three ways that we may estimate 
loadings: 1) estimate using watershed models augmented with point source, atmospheric, and 
oceanic inputs [i.e., USGS Spatial Referenced Regressions on Watersheds (SPARROW) model. 
Smith et al., 1997]; 2) use field-based water column concentration estimates during the 
biologically inactive portion of the annual cycle and then back calculate to loadings; and 3) use 


45 










field-based input studies (e.g. Cohn et al., 1989). SPARROW model based loading estimates are 
available from USGS for the larger watersheds throughout the nation and we will test those 
estimates in our response models and against field measurements when possible. However, for 
smaller systems, SPARROW estimates may not be available and different watershed models or 
alternative methods may be required. Where possible, NHEERL will also seek loading data from 
NERL to test other loading models and loading-relationships for coastal water bodies; 

Table 3. Preliminary list of factors influencing response to excess nutrient inputs in coastal 
receiving waters. 


Biological Factors 

Physical Factors 

Chemical Factors 

SAV 

Food web efficiency 
Phytoplankton community 
Primary productivity base 
(e.g., phytoplankton base vs. 
sea grass based) 

Grazing type (e.g., benthic 
filter feeders, zooplankton) 
and grazing intensity 

Flushing 

Light/Suspended 
solids/Water color 
Stratification 

Depth 

Temperature 

Volume 

Area 

Tidal Height 

Geomorphology (e.g., 
drowned river valley) 
Physical energy (wind, etc.) 
Hypsography (area-depth 
relationship) 

N:P:Si:Fe Ratios 

Salinity 

Allochthonous C 

Denitrification potential 
Nutrient form 
(organic/inorganic) 


however, if this is not possible, we may need to estimate loading in smaller systems by direct 
measurement (flow versus concentration). 

In addition, since water quality management is frequently based on returning to some historical 
loading or reference condition, to be most useful to OW, the load-response relationships should 
be based on historical loading to the maximum extent possible. 

Response Models 

Nutrient loading-response models are the ultimate products of the new research initiative. They 
will be produced using two parallel, yet integrated, efforts. Numerical models will be used to 
provide refinement of empirically derived loading-response models and to aid in understanding 
mechanisms. If our classification schemes are appropriate, they will identify groups of receiving 
water that have significantly less variability in nutrient load-endpoint response relationships than 
is present among all receiving waters. Improvement in these relationships will provide the test 
for our classification schemes (Step 3) and will provide the scientific basis for grouping receiving 
waters to simplify the nutriait criteria/TMDL process. 


46 






The classification schemes and modeling efforts will be endpoint-specific. For each endpoint in 
each of the regions and across all coastal systems, we will begin with a data mining effort and 
identify and collect data on key parameters and existing models and classification schemes that 
will provide the parts necessary to build a sound scientific basis for nutrient criteria for each of 
the three endpoints (DO, SAV loss, and food webs). With empirical data and knowledge of the 
key parameters that influence the nutrient-response curves, we can identify correlations and 
develop models to test hypotheses experimentally in the laboratory and in the field. This will be 
an iterative process to some extent until we have adequately characterized the endpoint/nutrient- 
response and controlling factor relationships needed to establish numerical nutrient criteria across 
the nation's coastal receiving waters. Once this is accomplished we can move on to watershed 
scales and/or other water body types. 

Complexity in ecosystem models is always dealt with by simplification; all models are 
abstractions of real systems. The simplifications range from complete linearization of the system 
with regression models to more complex relational models that incorporate a few critical 
measurements of the biological, chemical, and physical domain that makes up an estuary. Food 
web models make up the more complex non-linear models that include most of the mechanistic 
relations and feedbacks within the estuarine biogeochemical system. Relative to the coupled 
atmospheric-watershed-estuarine models developed for Chesapeake Bay and Long Island Sound, 
the food web models are easy to develop and have much fewer data requirements. 

Regression Models 

Regression models may be used to develop relationships among variables within a single system 
or among multiple systems. Such models use nutrient load to predict other parameters such as 
phytoplanktCMi biomass accumulation, primary production, sedimentation, and community 
metabolism, SAV loss, and DO-related response. This research will consist of cross-estuary 
analysis that focuses on common responses within classes of estuaries. Regression analysis that 
compares data among multiple systems has been used successfully for Maryland estuarine 
systems (Boynton et al. 1996); and a diverse collection of estuarine, continental-shelf, and open- 
ocean systems (Nixon et al. 1996). In the case of 37 side-embayments of Buzzards Bay, 
Massachusetts, such regression analysis has been applied to development of WQC and TMDLs 
for nitrogen (Costa et al. 1999). Such regressions are expected to have general application to 
systems similar to those for which they have been developed. These regressions will quantify 
estuary response to nutrient loading, and thus be directly useful in risk assessment and setting of 
nutrient criteria. In addition, regression models can be used as an adjunct to some of the 
proposed mesocosm and field work. For example, data from mesocosms to determine the effect 
of nutrient loading and benthic oxygen consumption on denitrification and nitrous-oxide 
production can provide insight into why some estuaries with nutrient sources having a high 
nitrogen/phosphorus (N/P) ratio remain nitrogen limited. Simple regression models also can be 
used in conservative mixing curves to determine sources and sinks of nutrients over the length of 
an estuary. Nitrogen, P, silicate, or other contaminants, when plotted against salinity, provide 
estimates of deposition, utilization, and supply of these materials over the length of an estuaiy. 

Vollenweider (1975) pioneered the development of regression models that allow extrapolation of 
data among systems using a scaling of biological processes to hydraulic residence time. This 


47 


method has been modified (Dettmann 2002) to model the fate and concentrations of total 
nitrogen in estuaries. Preliminary work in extending model applicability also has shown that 
total N concentrations predicted by the model appear to correlate well with peak annual 
phytoplankton concentrations and peak macroalgal abundance in estuaries. The overall goal of 
this work has been to explain the response of estuaries to nitrogen loading using as few 
parameters as possible. The model appears to reasonably describe annual net N, the dependence 
of annual denitrification on water residence time, and the annual average concentrations of total 
N in estuaries where it has been tested. The results emphasize the importance of water residence 
time in determining export, denitrification, and concentrations, and give quantitative expressions 
for these dependencies. At present, the model provides annualized results averaged over the 
entire estuary, although recent results indicate that it may also have application to seasonal 
response as well. The final extended model is expected to have direct applications to the 
evaluation of estuary sensitivity to nutrient loading, which will be useful in setting nutrient 
criteria. The model may also serve as part of the foundation of a classification system for 
estuarine sensitivity to nutrient loading. 

Food Web Models 

Models of food webs link the food web components to the overall ecosystem through an explicit 
quantification of exchanges. This makes it possible to evaluate how changes in the model 
components directly effect ecosystem processes. For example, the cycling of carbon and 
nutrients directly result from food web interactions in which many species play a role. Within 
many ecosystems, species that contribute little biomass still may have a laige influence on 
nutrient cycling and energy flow, and thus affect the functioning of other species. Examples of 
this include bacterial grazers, which can stimulate microbial activity through nutrient recycling, 
and algal grazers which stimulate the productivity of submerged macrophytes by providing better 
light conditions through the grazing of periphytic algae. Extinction or changes in abundance of 
such species can have a disproportionately large influence on ecosystem function. Hence, food 
web approaches can be used for analyzing the effects of nutrient stressors on key target species, 
on the biological diversity in communities, and on the functioning of ecosystems. In this way, 
food webs are the wiring on the circuit board of the ecosystem, spanning different levels of 
ecological organization. Food web models allow managers to identify the critical food web 
flows within the estuarine ecosystem, for which small changes in an ecosystem component will 
cascade through the system and result in large changes in eutrophication, extinction of important 
habitats, or changes in the tropic structure of the overall ecosystem (Vezina and Pace 1994). 

Food web models now being developed and evaluated in ORD are used to calculate metrics that 
define the state of an ecosystem (Ulanowicz 1986, Hagy 2002). These metrics are similar to 
diversity and commonness, but are much more sensitive to ecosystem condition than the older 
metrics. These new metrics can be calculated directly from the food web models now being 
developed at ORD. In the following two examples, we describe how these indices can be used 
to develop useful relationships between nutrient loading and trophodynamics: 

1. Indices of ecosystem trophic efficiency can be used to quantify how carbon and nutrients 
supplied to the estuary are passed thought the food web to the higher tropic levels. If more 
nutrients and carbon are moved into higher tropic levels, then the nutrient capacity of the estuary 


48 


may be increased. Laguna Madre during the 1990s provides an example of an ecosystem with 
small nutrient loading that developed symptoms of eutrophication. An inedible phytoplankton 
spQcxQS Aureoumbra lagunensis developed into a brown-tide bloom because most consumers 
could not ingest it. Those that could were being controlled by predators. Because of the 
mechanism by which this bloom developed, it could not be predicted easily by regression. 
However, susceptibility to such blooms could be predicted by food web models. 

2. Food web models can be used to calculate the dependency of charismatic and recreational 
species on other components of the food web, and how such dependency would be altered by 
changes in nutrient loading. This knowledge would arm managers with a eariy warning system 
to detect alterations in an ecosystem that eventually could lead to reductions or decreased 
production of important species. 

Each of these modeling strategies has different data requirements and makes predictions at 
different temporal and spatial scales. We will integrate the three approaches by working 
cooperatively to maximize the benefits of the approaches and minimize the limitations. The final 
outcome of this research implementation plan will be a set of empirical or numerical models for 
classes of coastal waters. These models will be able to accurately describe how increases in 
nutrient loading causes changes in hypoxia/anoxia in coastal receiving waters, losses of SAV, 
and changes in algal community composition leading to shifts in basic food webs. The models 
will provide the scientific basis for the development of nutrient criteria for coastal receiving 
waters. We should be able to extrapolate results between similar classes of receiving waters 
once a classification scheme incorporating key factors affecting nutrient response for coastal 
receiving waters is developed and tested. 

NHEERL Ecology Divisions are strategically located in four major coastal systems with AED 
along the Atlantic Coast, GED on the Gulf Of Mexico, WED along the Pacific Coast, and MED 
on the Great Lakes. Each of these Divisions will focus intensive studies on a local system and 
collect pertinent related information from other systems in their general region. All four 
Divisions will work to develop a common approach for key parameters and measurements that 
are needed across all regions (i.e., what, when, where, and how for nutrient loading, DO, 
Chlorophyll a) and maintain that approach through annual reviews. Other measurements (e.g., 
community metrics or depth of oxygen penetration into sediment) may be developed within or 
compared across multiple regions as individual Divisional research plans are developed further. 

Research Projects 

Project Title 1. Development of nutrient load-DO Response Relationships for Coastal 
Receiving Waters 

Project Coordination and Resources (9.0 FTEs: AED-4.0, GED-3.0, MED-1.0, WED-l.O) 


49 



Objectives 


To define nutrient load-DO response relationships for coastal receiving waters that will be used 
by the States and authorized Tribes to aid in the development of nutrient-related WQC and 
TMDLs. 

Scientific Approach 

Low DO in coastal receiving waters is a symptom of eutrophication. Therefore, controlling the 
effects of low DO is not accomplished by directly regulating DO but by regulating nutrients and 
oxygen demanding wastes. 

The ultimate product of this research effort is to provide the scientific basis to develop dissolved 
oxygen based nutrient criteria. The critical path to this product is: 

Step 1: Mine and assess existing information on DO response to excess nutrients for coastal 
receiving waters and minimum oxygen requirements of commercially and ecologically 
important organisms. 

Step 2: Develop conceptual model of how different systems manifest low DO in response to 
excess nutrients. 

Step 3: Propose a classification scheme for coastal receiving waters that groups these waters 
according to their sensitivity to DO depletion in response to excess nutrients. 

Step 4: Develop a common approach across Divisions. Select methods, parameters, and 
measurement endpoints for low DO response to excess nutrients so that data and 
models are interchangeable across Divisions and regions. 

Step 5: Test the proposed classification scheme to provide the scientific basis for development 
of nutrient criteria (or TMDLs) based on nutrient load-DO response relationships for 
different classes of receiving waters. 

Excessive nutrient loading to an estuary/receiving water stimulates primary production (i.e., 
phytoplanktonic, macroalgal). This production, together with allochthonous (labile) organic 
carbon, sinks to the bottom waters/benthos resulting in respiratory oxygen demand (mostly 
through microbial decomposition) that exceeds oxygen supply. This leads to hypoxia or anoxia 
in the bottom waters, which in turn lead to fish avoidance, fish kills, and mortality of sessile 
organisms such as worms and shellfish. Low oxygen also changes the oxidative properties of the 
sediments such that organic matter accumulates rather than being oxidized. Low oxygen can 
decouple nitrification and denitrification (xocesses in the sediments resulting in increased supply 
of nutrients to overlying waters. In addition, the death or debilitation of shellfish and other 
sensitive sessile organisms can cause changes in the structure and function of benthic habitats. 
For example, after hypoxic conditions dissipate, predators return to feed on vulnerable benthic 
organisms before they can rebound from the stressed conditions; this changes the energy balance 
of the system. 


50 


Hypoxic/anoxic conditions typically manifest themselves on diel or seasonal time scales during 
critical times of the annual cycle. For southem/semi tropical regions, the critical period is 
typically from May to October; for northem/temperate regions the period is from June to 
September. Seasonal hypoxia generally develops as a consequence of water column 
stratification, whereas diel cycles generally occur in non-stratified or partially stratified systems. 
Seasonal hypoxia is persistent throughout the critical period whereas diel hypoxia may be cyclic 
(regular frequency and duration; e.g., diel, tidal) or episodic (irregular frequency and duration). 

DO Criteria 

Part of the process of setting nutrient criteria based on DO involves determination of the 
minimum DO requirements of aquatic organisms. NHEERL is in the process of providing the 
scientific basis for setting minimum DO criteria for ecologically and commercially important 
organisms in coastal receiving waters through laboratory exposures. Survival data from 
laboratory exposures, using controlled DO concentrations, will provide risk assessment managers 
with the basic information needed to set minimum DO protection limits for the Nation’s waters. 

General Classification Variables 

The classification scheme for the DO endpoint will be a common effort across all Divisions. Our 
plan is to link and improve existing models of flushing, light limitation, primary production 
controls, and oxygen supply dynamics to sort coastal receiving waters into groups of similar 
overall relative sensitivity. The number of groups will depend on the range and variability of 
estimates. Chapter 6 of Clean Coastal Waters (NRC 2000) lists 12 factors that influence the 
susceptibility of coastal receiving waters to nutrient over-enrichment. We will start with this list 
and focus on those factors that will be most useful in a classification scheme to determine the 
relationship between nutrient loading and DO as an effect of nutrient over-enrichment. The 
relative magnitude of atmospheric oxygen entrainment to the bottom waters and respiratory 
depletion determines whether a portion of the water column experiences episodes of low DO. 

The most important factors that affect entrainment include: density, salinity, and/or temperature 
stratification. In turn, these factors are influenced by climate/weather (temperature, wind), 
geomorphology (tides), and circulation patterns (tides and freshwater input) of the receiving 
waters. 

Once the classification scheme with the key parameters influencing the response of DO to 
nutrients has been developed, we will test the classification using field data and comparable data 
from literature or other institutions and agencies where available. Both empirical and numerical 
simulation models will be used to improve our understanding of how these parameters interact 
and control the DO response to increased nutrient supply across a wide range of coastal receiving 
waters. 

Measurement Endpoints 

As mentioned above, "low DO" is one of the assessment endpoints that is to be related to excess 
nutrients in receiving waters. There are many methods and approaches to determine DO in 
coastal receiving waters. Providing sound basis for a national nutrient criteria based on DO will 


51 


require development of a common approach to be applied and tested across a wide range of 
coastal receiving water systems. Each NHEERL Ecology Division will need to contribute to this 
process, and to the extent possible identify other soiffces of similar data from receiving waters in 
their region that could be used to improve our understanding of the relationship between nutrient 
loading-DO and the key factors that control that relationship. 

Products 

APM 1C FY02 (GPRA # 165) Minimum dissolved oxygen requirements of aquatic animals in 
the Gulf of Mexico estuaries as a measure of the effect of nutrient enrichment (GED). 

APM 2A FY03 Minimum DO requirements for a suite of the important marine organisms (fish 
and crustaceans) from the Atlantic, Pacific, and Gulf of Mexico coastal waters of the U.S. 

(AED). 

APM 4A FY03 Propose classification scheme for predicting sensitivity of coastal receiving 
waters to effects of nutrients on DO (MED, AED, GED). 

Benefits of Products 

The benefits of the products to OW will be a reduction in the uncertainty associated with setting 
DO based nutrient criteria and TMDLs for our nation’s receiving waters. Minimum DO 
requirements of important species will provide a sound basis for setting protective limits for DO 
in coastal receiving waters. Development of an improved classification scheme will aid in 
setting nutrient criteria in receiving waters where large historical databases are not available. An 
improved understanding of the factors affecting nutrient DO-response relationships will provide 
water quality managers with better tools to manage nutrient input to our nation’s waters. 

Project Title 2, Development of SA VLoss-Nutrient Load Relationships and Factors which 
Control SAV Response to Nutrients 

Project Coordination and Resources (9.0 FTEs: AED-1.0, GED-4.0, MED-2.0, WED-2.0) 
Objectives 

The objective of this research plan is to develop, for the nations’s coastal receiving waters, 
sufficient understanding of the relationship between SAV loss and nutrient loading (N and P) to 
provide a sound scientific basis for establishment of nutrient criteria that would protect these 
important habitats from degradation or loss and aid in restoration efforts. To do this, a set of 
models will be used to examine how nutrients interact with the physical and biological 
components to affect the health of SAV populations. This work will directly support or interact 
with the habitat alteration research (Section 4) providing basic information on production and 
function of these habitats and diagnostic research (Section 8) by providing nutrient-SAV- 
response relationships. 


52 


Scientific Approach 

Research on SAV survival and production has generally focused on a few "key” parameters (e.g., 
light, nutrients), and much is known about the basic light requirements of SAV. Nutrients 
primarily affect SAV through their effects on water quality and the associated effects on light 
availability caused by increasing algal biomass (pelagic and epiphytic). Light availability is 
generally considered to be the major factor related to SAV survival (e.g., Kenworthy and Haunert 
1991, Tomasko and Lapointe 1991, Fourqurean and Zieman 1991, Dennison et al. 1993, 
Stevenson et al. 1993, Dunton 1994) and depth of distribution (Dennison 1987, Dawes and 
Tomasko 1988, Duarte 1991). However, recent work is beginning to suggest that a more holistic 
approach may help delineate additional fectors and improve our understanding of the relationship 
between nutrients and SAV loss (Koch 2001, Kaldy et al. 2002). In addition to understanding the 
requirements of SAV, we must also understand the interactions of nutrients and the physical, 
chemical, and biological factors that control accumulation of phytoplankton biomass in our 
receiving waters. This is similar to research needed to understand the relationship between 
nutrients and hypoxia (project 1); however, there are some differences in the factors associated 
with accumulation of biomass versus the increase in production associated with hypoxia. 

The approach will be consistent with the general critical path stated above. Essentially, the main 
components for this research are: 

1. Data gathering, literature review, and compilation of scientific literature (including existing 
information on SAV models for coastal systems) will focus on SAV-nutrient relationships. 

Where existing nutrient-SAV community data are available, this will include the compilation and 
statistical analysis of data. Over the last 30 years, a large body of literature has been published 
with regard to seagrass and other SAV. These publications should provide strong guidelines for 
directing EPA research. As part of this step, we plan to develop a report on the basic 
requirements (e.g., light, salinity, sediment characteristics) of three rooted aquatic seagrasses 
(Thalassia, Halodule, and Zoster a) which together represent a large fraction of the SAV found in 
estuarine/marine systems along the coast of the U.S. Our research on SAV loss-nutrient load 
relationships will not be limited to these three seagrasses and will include freshwater and 
brackish rooted SAV as well. 

2. Develop conceptual models of how SAV communities and nutrients interact with various 
environmental parameters to result in decreased survival or production in this important habitat. 
These models may be regionally specific because of the inherent differences; however, we will 
seek to make them as broadly applicable as possible. Our current conceptual model of how 
nutrients are involved in SAV loss suggests that increased nutrients leading to increased algal 
biomass accumulation is the basis for this pathway. The most likely factors affecting algal 
biomass accumulation are flushing rates, grazing rates, turbidity, climatic conditions, and water 
depth. We realize that excess nutrients also can cause nitrate toxicity, and increase sediment 
sulfide concentrations and/or toxicity. 

3. Propose a classification scheme for coastal receiving waters that groups these waters 
according to their sensitivity to SAV loss in response to excess nutrients. Evaluate and modify 
existing models in order to develop nutrient-SAV loss responses. Classification parameters will 


53 



certainly include dominant SAV species as well as many of the parameters listed in Clean 
Coastal Waters (NRC 2000). Development of a classification scheme for the SAV endpoint will 
be a common effort across all Divisions. Our plan is to use knowledge of species-specific 
requirements and link existing models or develop models that include the effects of light, 
nutrients, and sediment geochemistry on seagrass physiology. We also will link these with 
models of water column chlorophyll a-light absorption/attenuation and nutrient-phytoplankton 
biomass relationships to provide a basis for setting nutrient criteria for coastal receiving waters 
where protection or restoration of SAV is needed. 

4. Develop a common approach across Divisions. Select methods, parameters and measurement 
endpoints for SAV response to excess nutrients so that data and models are interchangeable 
across Divisions and regions. Each Division will provide measures of the key parameters (the 
final list of these parameters will be developed and standardized); however. Division-specific 
research plans may focus on specific parameters or measurements as part of individual research 
projects (e.g., epi-periphyton community metrics, sediment sulfide production/toxicity). This 
phase of the plan development will include interaction with seagrass and SAV specialists in other 
governmental and academic institutions where possible. 

5. The final step of this SAV critical path is a verification that tests the model’s prediction and 
our classification scheme against the actual endpoint response. Once tested and verified, the 
proposed classification scheme and models will provide the scientific basis for development of 
nutrient criteria or TMDLs based on nutrient load-SAV response for different classes of 
receiving waters. 

Modeling Plan 

Our modeling approach for the seagrass endpoint is embodied in a three-tiered scheme that 
couples model development, field monitoring, and direct experiments to test specific hypotheses 
(Figure 6). We plan to use both holistic and mechanistic approaches that integrate the scientific 
literature and conceptualize how seagrasses are affected by stressors by using numoical and 
empirical models that quantify the production and distribution of seagrass. Characterization of a 
suite of parameters (e.g., light, sediment biogeochemistry, nutrients, and exposure during part of 
the tidal cycle) and their inclusion in models and classification schemes will allow EPA to 
determine which stressors are most important controlling factors in a particular region. Stressors 
which will impact SAV vary between regions as a result of variations in climate, industry, 
agriculture, and other land use practices. Consequently, it is critical to include a wide variety of 
parameters that influence all SAV (seagrass and freshwater aquatics), regardless of region. The 
appropriate scientific approach will likewise be an approach combining model development, 
field monitoring, and direct experimentation to test specific hypotheses generated by the models. 

Built into this SAV modeling plan is a set decision point used to determine if the conceptual 
model, the numerical or empirical model, or the field test of the model has been successful. This 


54 


SAV 

Model Development 


Data Mining 

N Load or proxy, Historical SAV coverage, 
Avg Chi a as proxy for phytoplankton, 
existing models (I V Chi, Kd vZ by species, 
N V SAV Loss by system) _ 




Define Conceptual Model 


Develop Numerical Model 
To Test Conceptual Model 




Develop Empincal Model 
To Test Conceptual Model 


Example cf parameters 
required for an existing 
biomass model 

Totfcl PAR time Ode, 

TS S, Color, Sp ectnlmo del 
Historiceltzulcutxetit 
c ownge, Above/be low 
groundbicm&ss,P ^/P^.P 
vs.I Q]rve,NutrimlT9)iul(c 
( C/HX Temperetur e, Leef 
loss nte ,Poie weter H,D«a 
essimiliticn te dmique 


D oes Mo del Make Sense? 
Yes 

_ i _ 


No 


Field Test Model 


Does Field Test Validate Model? 



Nutrient Criteria For SAV 


7 ^ 


Figure 6. Conceptual diagram of the feedbacks among data mining, model development, field 
monitoring, and experimental hypothesis validation. 


iterative process assures that at each stage in the critical research path, error in data and models 
are sufficiently small that the completed analysis will be accurate enough to make meaningful 
prediction of the SAV response to nutrients. 

Model Development 

Seagrass models are generally a composite of numerical and empirical relationships that provide 
a quantitative prediction of seagrass growth or loss. Each of these components has to be tested 
individually and in concert with other relationships that make up the model. Although all SAV 
models will have components in common, each regional model will be individualized to 
incorporate locally important species, the biogeochemistry of the water-column and sediments. 


55 
















and the local physical regime (Table 3). Successful model development will require long-term 
continuous data sets using instruments to measure important plant parameters (e.g., spectral 
irradiance, temperature, and salinity). It is critical that monitoring be conducted at appropriate 
temporal and spatial intervals that are relevant to the organism(s) and system(s) in question. 
Development and implementation of any long-term monitoring plan for use in modeling 
activities should be a cooperative effort involving the appropriate personnel (e.g., modelers, 
biologists, and field technicians). 

Field Monitoring 

The purpose of the field monitoring portion of this plan is to collect data on the range of 
responses and variability that are present in coastal systems and to provide input data to generate 
or refine models. Aquatic environments in general, and particularly estuaries, are stochastic 
systems that often exhibit large variations along many temporal and spatial scales. Long-term 
data sets are required to determine if variability expressed in a system is a consequence of natural 
variability (e.g., storm events) or anthropogenic impacts. 

Direct Experimentation (Field and/or Mesocosm Studies) 

One of the most important features of a model is the ability to develop testable hypotheses that 
will provide confidence in the model. In most cases, field experiments would be the preferred 
experimental environment; however, it is often difficult if not impossible to control all of the 
variables in the field (e.g., water column nutrient concentrations). The ability to replicate 
treatments also permits statistical data analysis. Consequently, mesocosm experiments bring 
together the best features of field and laboratory experiments offering environmental parameter 
control and a natural environment, while facilitating quantitative data collection (for review see 
Lain 1990). 

Development of the proposed models would provide testable hypotheses about the influence of 
any number of factors, such as the toxicity thresholds of SA V to various water column and 
sediment constituents (e.g., nitrate, ammonium, or sulfide concentrations). Another example 
would be determination of dessication stress, or sediment and water column anoxia on 
photosynthesis, or the interactions controlling the relationships between nutrients and 
phytoplankton versus epiphytes versus macroalgae versus seagrass. Physical factors such as the 
impact of wave exposure could also be investigated using replicated mesocosm experiments. 

Current Activities 

AED is developing empirical relationships between nitrogen loading and the areal extent of SAV 
normalized to historical SAV habitat extent. MED is investigating the relationship between 
nutrient loading and several quantitative attributes of wetland SAV including: % cover, diversity, 
relative abundance, and maximum depth of macrophyte growth. GED is in the fourth year of 
developing a database of changes on water quality, light availability, and changes in the 
deepwater margin of SAV beds. WED is developing models of seagrass growth and production 
based on field data including seagrass biomass and production, underwater light, and sediment 
biogeochemistiy. Other activities that support this SAV plan include GIS mapping of seagrass 


56 


distribution over multiple annual cycles and the development of spatially explicit models to 
examine seagrass bed dynamics (e.g., expansion). Skills and knowledge represented by these 
research projects will be integrated across Divisions and will provide the basis for comparison 
and testing of approaches outlined above across the nation’s SAV communities. Again, to be 
most useful to OW, SAV coverage and the loading response relationships should be based on 
historical information to the maximum extent possible since water quality management is 
frequently based on returning to some historical reference condition (realizing that such data is 
often not available). 

Products 

APM 1A FY02 (GPRA # 163) Generalized seagrass/rhizosphere model capable of predicting 
effects of reduced light, sedimentation, nutrient depletion, and toxic effects of sulfides (WED). 

APM IB FY02 (GPRA # 164) Effects on estuarine submerged aquatic vegetation from changes 
in light quantity and quality due to increased levels of suspended solids (GED). 

APM 5A FY02 Report on structural and functional characteristics of SAV rhizospheric 
communities (GED). 

APM 5B FY03 Correlation of water quality with SAV change (GED). 

APM 5C FY04 Report on environmental requirements of three main species of seagrasses 
(WED). 

APM 5D FY04 Development of stress-response model fox Zostera marina in Pacific Northwest 
and validation of stress-response model for Thalassia testudinum (WED). 

APM 5E FY04 Development of empirical load-response models for Zostera marina in NE U.S. 
(AED). 

APM 5F FY05 Development of load-response models for estuaries of Pacific Northwest and 
Gulf Coast, and validation of stress-response model for Zostera marina in NE U.S. (WED, 
GED, AED). 

APM 5G FY05 Propose classification scheme for predicting sensitivity of coastal receiving 
waters to the effects of nutrients on SAV (WED, GED, AED). 

APM 5H FY06 Report on the empirical and numeric models for SAV (WED, GED, AED). 

APM 51 FY07 Report on a classification scheme for grouping coastal receiving waters based on 
sensitivity to nutrients.(WED, GED, AED). 


57 



Benefits of Products 


The benefits of the products will be a reduction in the uncertainty associated with setting SAV 
based nutrient criteria and TMDLs for our nation’s receiving waters. A compendium of 
requirements of important SAV species will provide a convenient reference and a sound basis for 
setting protective limits in coastal receiving waters. Development of a classification scheme will 
aid in setting nutrient criteria in receiving waters where large historical databases are not 
available. An improved understanding of the factors affecting nutrient-SAV loss relationships 
will provide water quality managers with better tools to manage nutrient input to our nations 
waters while protecting these important habitats. 

Project Title 3, Food Web and Community Composition Changes in Response to Nutrient 
Loading in Freshwater and Marine Coastal Systems (Estuaries and Coastal Wetlands) 

Project Coordination and Resources (14 FTEs: AED-3.0, GED-5.0, MED-3.0, WED-3.0) 

Objectives 

The primary objective of food web research is to identify nutrient loading thresholds that cause 
shifts in primary producers and other key components of the food web. A secondary objective is 
to assess the use of food web structure and processes to improve our ability to classify systems 
and to predict changes in response to nutrients that affect hypoxia and SAV. Research will 
require identification of measurement endpoints that are sensitive to nutrient loading and reliably 
forecast adverse effects to assessment endpoints. 

Scientific Approach 

As with the DO and SAV assessment endpoints for nutrient research, we are concerned that 
increased concentrations or changes in ratios or timing of nutrient inputs can adversely affect 
populations of ecologically and commercially important organisms. For food webs, nutrient 
loading-response relationships are not as well understood as for DO and SAV; however, food 
webs may reveal subtle, low threshold responses to nutrient loading that are more sensitive than 
DO or SAV endpoints (Livingston 2000). Changes in patterns of energy flow alter habitats and 
support systems required by important organisms. Variations in nutrient ratios, ccmcentrations, 
or timing of inputs can alter the competitive advantages of primary producers causing the demise 
of species on which important consumers depend. Research investigating the relationships 
between nutrient loading and food webs will focus on identifying thresholds of nutrient loading 
where pathways of energy flow from primary producers to consumers are altered and populations 
of commercially and ecologically organisms are adversely affected. NHEERL research will 
focus on the effects of nutrients on primary producers and subsequent interactions with pelagic 
and benthic communities. Process-oriented research, such as analysis of food web structure via 
stable isotopes will provide basic data for development of nutrient load-response relationships, 
and will provide insight into biological factors controlling primary production and energy flow in 
coastal systems. This research will focus on three critical food web shifts: 1) changes from 
benthic to pelagic basis of production (i.e., SAV or periphyton to phytoplankton), 2) shifts in 


58 


pelagic algal communities from desirable (edible, nutritious) to undesirable (non-edible, 
nuisance, HAB species), and 3) shifts in emergent or marsh grass systems. 

Major challenges of this research are to identify practical measurement endpoints (response 
variables) and relate those responses to assessment endpoints (e.g., high performance liquid 
chromatograhy [HPLC] accessory pigments may be useful as measure of changes in the 
phytoplankton community leading to or associated with decreased fisheries production). We will 
investigate development of indices of tropic status associated with high nutrient conditions, such 
as ratios of algal biomass to zooplankton and/or fish, benthic and pelagic community metrics that 
reflect algal composition (e.g., percent blue-greens, and relative abundance of centric and 
pennate diatoms, algal size distributions). Developing load-response relationships, which reflect 
changes in food web structure, will be challenging due to the complex feedback mechanisms 
which accompany degraded habitats in coastal systems (Jude and Pampas 1992, Chow-Frazer 
1998). Increasing phytoplankton biomass may uncouple primary production from grazing. This 
then leads to algal blooms, possibly toxic or noxious algae (HABs) and successions in pelagic 
and benthic communities. The causal mechanisms for HABs remain poorly understood; some 
have always occurred and are entirely natural. However, other blooms are tied to nutrient 
enrichment, thus leading to more frequent and longer lasting blooms as nutrient loading increases 
(NRC 2000). Even more uncertainty exists regarding relationships between nutrient loading and 
basic changes in food webs supporting productive marine and freshwater ecosystems. In addition 
to nutrients, the activity of top consumers can exert strong controls on zooplankton and/or 
phytoplankton affecting phytoplankton size, abundance, and production. Biogeochemical 
processes resulting from blooms (i.e., enhanced sedimentation and redox changes) can cause 
changes in species diversity, size spectrum of organisms, and average tropic level of the 
community. The secondary effects of these water-column and sediment changes may be 
persistent changes in pelagic and benthic species assemblages and alterations in the nutrient 
recycling potential of aquatic habitats. Our approach is to determine nutrient load-response 
thresholds for endpoints reflecting shifts in the food web structure or species composition. 

The Scientific approach will be consistent with the general critical path (Figure 5). Essentially, 
the main components for this research are: 

1. Data gathering and literature review of food web information for coastal food web systems 
will be done collaboratively across Divisions, where existing nutrient-food web/community data 
are available, statistical analysis will be conducted. 

2. Conceptual models that include both bottom up effects models and bottom up-top down 
community models will be developed. The bottom up model (nutrients to primary production, 
Menge 2000) shows influences on aquatic communities, including the formation of some HABs 
and ultimately changes in important fish and shellfish populations. The community level model 
includes interactions among populations that form the food web and identify critical interactions 
that may lead to changes in species diversity, commercial harvest, eutrophication, and designated 
use. 

3. A classification scheme for coastal receiving waters that groups these waters according to 
their sensitivity to food web changes in response to excess nutrients will be provided. Existing 


59 





food web models will be evaluated and modified in order to develop nutrient-food web responses 
incorporating classification schemes. 

4. We will develop common approaches across Divisions for parameters related to food web 
shifts (e.g., HPIX pigment analysis, stable isotope measurements, community metrics). We will 
select common methods, parameters, and measurement endpoints for food web response to 
excess nutrients when available, so that data and models are interchangeable across Divisions 
and regions, which will provide the basis for comparison and testing of our models, methods, and 
classification schemes. 

5. Testing of the proposed classification scheme and models will provide the scientific basis for 
development of nutrient criteria and/or TMDLs based on nutrient load-food web response for 
different classes of receiving waters. 

Initial research projects adopted by all four Ecology Divisions, to collect data for identifying 
potential assessment endpoints or testing classification schemes and models, will help in the 
development of a better understanding of the processes leading to shifts in food webs and help 
identify factors and parameters in common with the process. Comparison of results and 
application of successful techniques across Divisions will speed identification of practical 
assessment endpoints and development of models and understanding of food web-nutrient 
relationships. We plan to use stable isotope measurements in systems with various types of 
dominant primary producers at the base of the food web (i.e., pelagic phytoplankton, saltmarsh, 
benthic algae, and SAV). Indices that we plan to predict include ascendancy and/or flow 
diversity. This indice predicts the relative stability of food webs. System with low flow diversity 
tend to have large temporal variations in structure, usually at the lower tropic levels, but 
sometimes at the mid and high tropic levels. In the long-term, these systems eventually come to 
a new stable state that may be more eutrophic than the original food web. Hence, we can predict 
what systems are at risk (unstable) even though no model can reliably predict species succession. 
Nutrients as a stressor can reduce food web flow diversity and the ensuing systems instability 
may result in HABs, macroalgae, and other harmful species. With sufficient data in the various 
system types, we can use optimization techniques to correlate changes in flow diversity (stability) 
with nutrient load. 

WED-Food web models will be developed that track nutrient and carbon flow through aquatic 
systems using a combination of stable isotope and population level data. This is a community 
level model designed specifically to identify critical food web interactions that lead to changes in 
species diversity, commercial harvest, eutrophication, and other factors that could effect the 
designated use of a habitat. This stable isotope based model will be used to compare energy flow 
in the different types of coastal systems where each of the Divisions are located. Application of 
this model across all four Divisions will provide insight into carbon and nutrient processing 
across a wide variety of systems. 

GED-Will provide stable isotopre measurements to test the WED food web model and will focus 
on factors controlling shifts in the phytoplankton community structure and size in Escambia Bay, 
FL, which is dominated by pico-cyanobacteria blooms in summer. Size of phytoplankton is an 
important factor that determines the structuring of both the pelagic and benthic food webs. 


60 


Picoplankton is associated with a microbial food web while nanoplankton is associated with 
zooplankton consumers and commercially and charismatically important higher tropic levels. 
This research will provide insight into the chemical and biological factors controlling pelagic 
phytoplankton community. 

AED-Will also measure stable isotope shifts and will provide a test of the WED food web model 
including average food chain length. AED will investigate the effects of nutrients on saltmarsh 
species changes and generate methods to relate nutrient loading to chl-a levels using remote 
sensing, (i.e., airframe and satellite spectral analyses), including the ability to distinguish between 
different types of phytoplankton blooms (diatoms vs. dinoflagellates). 

MED-Will initially focus on two of the Great Lakes, Lake Superior, and Lake Michigan. 

In addition to using stable isotopes to investigate food web structure in Great Lakes coastal 
wetlands, nutrient effects oriented research will be employed to investigate several assumptions: 

• Algal-zooplankton size distribution in response to nutrient loading, 

• Algal community responses to N/P ratios in freshwater coastal wetlands, and 

• Modeling efforts focusing on establishing relationships of nutrient loadings and ambient 
concentrations with chlorophyll, DO, N/P ratios, phytoplankton species composition, 
food chain productivity, and water column transparency. 

In addition, where possible, we will partner and collaborate with other coastal nutrient efforts 
(i.e.. State of the Lake Ecosystem Conference [SOLEC], GLEI, Atlantic Coast Environmental 
Indicators Consortium, Great Lakes Coastal Initiative, States, and Tribes). 

Products 

APM ID FY 02 (GPRA # 166) Effects of nutrient loadings and altered nutrient ratios on HABs 
(GED). 

APM 3 (GPRA #201) Comparison of effects of zooplankton grazing on estuarine phytoplankton 
community under differing natural levels of grazing (GED). 

APM 6A FY04 Sensitivity of food web responses to nutrient loading in coastal systems (GED). 

APM 6B FY05 Propose classification scheme for predicting sensitivity of coastal receiving 
waters to effects of nutrients on food web structure (GED). 

APM 6C FY06 Report on empirical and numeric models for food webs (WED). 

APM 6D FY 06 Report on parameterization of food web models (GED). 

APM 6E FY06 Report on classification scheme for grouping coastal or lake receiving waters 
based on sensitivity to food web alterations (WED, GED, AED, MED). 


61 




These products will include: 

FY03 State of the Science report on nutrient food web relationships in Coastal Systems (AED, 
GED, MED, WED). 

FY04 Interim report on sensitivity of food web response to nutrient loading in coastal systems 
(AED, GED, MED, WED). 

FY05 Recommendation on use of food web related endpoints to predict effects of nutrients on 
important fish and shellfish populations (AED, GED, MED, WED). 

Benefits of Products 

This research will provide the basis for setting ecologically relevant nutrient criteria for 305b 
reporting and TMDL development that supports the protection of aquatic life as mandated under 
CWA. By providing standardized methodology, it will provide guidance to the States and EPA 
Regions for developing appropriate monitoring protocols. In addition, a better understanding of 
nutrient-food web response relationships will significantly improve our ability to predict 
ecosystem response to other nutrient endpoints (DO and SAV loss). 

Gap Analysis 

In order to focus on what can be accomplished with the available resources, we have chosen the 
four coastal regions described above. There are other major coastal regions we are not covering 
such as Mid and South Atlantic systems or Southern Pacific coastal waters. In addition, this 
research is focused on coastal receiving waters. It does not directly focus on understanding 
nutrient response relationships in streams, rivers, lakes, inland wetlands, or headwater seeps, for 
which additional research is needed. Longer range plans do include an integrated watershed 
approach as resources become available. We are not developing DO criteria for any freshwater 
species of fish or SAV light requirements for freshwater SAV. 

Currently we do not have sufficient understanding of how the biological components of an 
ecosystem interact to process nutrients to be able to predict how differences in these components 
affect the capacity of an ecosystem to assimilate nutrients. To do this more effectively, we need 
to improve our skills in the area of ecosystems ecology and ecosystem modeling. In addition, the 
presence of a database manager to establish a central database or linkages between Divisional 
databases would significantly improve the transfer and sharing of data to be used and tested in a 
variety of approaches (models and classification schemes). It is hoped that NERL will provide 
nutrient loadings for receiving water bodies that will be considered by NHEERL; however, if this 
is not possible then NHEERL will need to determine or estimate loadings. 

References 

Boynton, W.R., Hagy, J.D., Murray, L., Stokes, C., Kemp, W.M. 1996. A comparative analysis 
of eutrophication patterns in a temperate coastal lagoon. Estuaries 19B:408-421. 


62 


Boynton, W.R., Kemp, W.M. 2000. The influence of river flow and nutrient loads on selected 
ecosystem processes. In Hobbie, J., ed.. Estuarine Science: a Synthetic Approach to Research 
and Practice. Island Press, Washington, DC, pp. 269-298. 

Chow-Frazer, P., 1998. A conceptual model to aid restoration of Cootes Paradise Marsh, a 
degraded coastal wetland of Lake Ontario, Canada. Wetland Ecol. Manag. 6:43-57. 

Cohn, T.A. Delong, L.L. Gilroy, E.J. Hirsch, R.M. Wells, D.K. 1989. Estimating constituent 
loads. Water Resources Research 25:937-942. 

Costa, J.E., Howes, B.L., Janik, D., Aubrey, D., Gunn, E., Giblin, A.E. 1999. Managing 
anthropogenic nitrogen inputs to coastal embayments: technical basis and evaluation of a 
management strategy adopted for Buzzards Bay, Buzzards Bay Project. 

Dawes, C.J., Tomasko, D.A. 1988. Depth distribution of Thalassia testudinum in two meadows 
on the west coast of Florida; a difference in effect of light availability. Mar. Ecol. 9:123-130. 

Dennison, W.C. 1987. Effects of light on seagrass photosynthesis, growth and depth distribution. 
Aquat. Bot. 27:15-26. 

Dennison, W.C., Orth, R.J., Moore, K.A., Stevenson, J.C., Carter, V., Kollar, S., Bergstrom, 
P.W., Batiuk, R.A. 1993. Assessing water quality with submersed aquatic vegetation. BioScience 
43:86-94. 

Dettmann, E.H. 2002. Effect of water residence time on annual export and denitrification of 
nitrogen in estuaries: a model analysis. Estuaries (in press). 

Duarte, C.M. 1991. Seagrass depth WmxXs. Aquat. Bot. 40:363-377. 

Dunton, K.H. 1994. Seasonal growth and biomass of the tropical seagrass Halodule wrightii in a 
hypersaline subtropical lagoon. Mar. Biol. 120:479-489. 

Eldridge, P.M. Jackson, G.A. 1993. Benthic tropic dynamics in California coastal basin and 
continental slope communities inferred using inverse analysis. Mar. Ecol. Prog. Ser. 99:115-135. 

EPA. 2000. Ambient water quality criteria for dissolved oxygen (saltwater): Cape Cod to Cape 
Hatteras. EPA-822-R-00-012. Office of Water, Washington, DC. 

Fourqurean, J.W., Zieman, J.C. 1991. Photosynthesis, respiration and whole plant carbon budget 
of the seagrass Thalassia testudinum. Mar. Ecol. Prog. Ser. 69:161-170. 

Hagy, J.D. 2002. Eutrophication, hypoxia and trophic transfer efficiency in Chesapeake Bay. 
Ph.D Dissertation, University of Maiyland, College Park, MD. 

Jude, D.J., Pampas, J. 1992. Fish utilization of Great Lakes coastal wetlands. J. Great Lakes Res. 
18:651-672. 


63 





Kaldy, J.E., Dunton, K.H., Kowalski, J.L., Lee, K.S. 2002. Evaluation of environmental factors 
controlling the success of seagrass revegetation onto dredged material deposits: a case study in 
Lower Laguna Madre, Texas. Restor. Ecol. (in revision). 

Kenworthy, W.J., Haunert, D.E. 1991. The light requirements of seagrasses: proceedings of a 
workshop to examine the capability of water quality criteria, standards and monitoring programs 
to protect seagrasses. NMFS-SEFC-287. NOAA technical memorandum. 

Koch, E.W. 2001. Beyond light: physical, geological and geochemical parameters as possible 
submersed aquatic vegetation habitat requirements. Estuaries (in press). 

Lain, C.M., ed. 1990. Enclosed Experimental Marine Ecosystems: a Review and 
Recommendations. Springer-Verlag, New York. 

Livingston, R J. 2000. Eutrophication Processes in Coastal Systems: Origins and Succession of 
Plankton Blooms and Effects on Secondary Production in Gulf Coastal Estuaries. CRC Press, 
Boca Raton, FL. 327 pp. 

Menge, B.A. 2000. Top-down and bottom-up community regulation in marine rocky intertidal 
habitats. J. Exp. Mar. Biol. Ecol. 250:257-289. 

Nixon, S.W. 1995. Coastal marine eutrophication: a definition, social causes, and future concerns 
Ophelia A\\\99-2\9. 

Nixon, S.W., Ammerman, J.W., Atkinson, L.P., Berounsky, V.M., Billen, G., Boicourt, W.C., 
Boynton, W.R., Church, T.M., Ditoro, D.M., Garber, J.H., Giblin, A.E., Jahnke, R.A., Owens, 
N.J.P., Pilson, M.E.Q., Seitzinger, S.P. 1996. The fate of nitrogen and phosphorus at the land- 
sea margin of the North Atlantic Ocean. Biogeochemistry 3>S: 141-180. 

NRC. 2000. Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient 
Pollution. National Academy Press, Washington, DC. 

Smith, R.A. Schwartz, G.E. Alexander, R.B. 1997. Regional interpretation of water quality 
monitoring data. Water Resources Research 33:2781-2798. 

Stevenson, J.C., Staver, L.W., Staver, K.W. 1993. Water quality associated with survival of 
submersed aquatic vegetation along an estuarine gradient. Estuaries 16:346-361. 

Tomasko, D.A., Lapointe, B.E. 1991. Productivity and biomass oiThalassia testudinum as 
related to water column nutrient availability and epiphyte levels: field observations and 
experimental studies. Mar. Ecol. Prog. Ser. 75:9-17. 

Ulanowicz, R.E. 1986. Growth and Development: Ecosystem Phenomenology. Springer-Verlag, 
New York. 203 pp. 


64 


Vezina, A.F., Pace, M.L. 1994. An inverse model analysis of planktonic food^vebs in 
experimental lakes. Can. J. Fish. Sci. 51:2034-2044. 

Vollenweider, R.A. 1975. Input-output models with special reference to the phosphorus loading 
concept in limnology. Schweiz. Z. Hydrol. 37:53-84. 


65 




Section 6. 

Implementation Plan for Suspended and Bedded Sediment Research 


NHEERL's effort concerning suspended and bedded sediments has been redirected since this 
section was first written. The majority of the work in this research area will now occur under 
Goal 8 (EMAP) because EMAP design techniques will be applied to develop effect thresholds 
for suspended and bedded sediments in aquatic systems. Some of these techniques are described 
generally in the Critical Path subsection of this implementation plan. However, at this time, the 
effort under aquatic stressors will only include a literature review of suspended and bedded 
sediments research. Results from this review will be combined with EMAP app’oaches to 
synthesize and evaluate the state of the science. Once the review has been completed, data gaps 
will be identified and additional research will be conducted, if warranted. 

Probleih 

The Office of Water has identified suspended and bedded sediments as one of OW’s highest and 
most immediate priorities. The priorities within aquatic systems for developing these criteria 
were identified as: rivers and streams; followed by lakes, reservoirs, ponds, and estuaries. For 
purposes of this document, suspended sediments are those sediments that exert their negative 
impact via their suspension in the water column, such as the effect of shading induced by them 
on submerged macrophytes. Bedded sediments are those sediments that have their negative 
impact when they are actually settled out and on the bottom of the water body of interest such as 
fine sediments which smother spawning beds. Research in this section does not deal with 
contaminated sediments (those containing toxic chemicals, see Section 7). 

In streams and rivers, fine inorganic sediments, especially silts and clays, affect both the habitat 
for macroinvertebrates and fish spawning, as well as fish rearing and feeding behavior. Larger 
sands and gravels can scour diatoms and cause saltation of invertebrates, whereas suspended 
sediment affects the light available for photosynthesizing plants and visual capacity of animals 
(Waters 1995). A major problem with suspended sediment in coastal wetlands, estuaries, and 
near-shore zones is the decreased light penetration which often causes aquatic macrophytes to be 
replaced with algal communities, with resulting changes in both the invertebrate and fish 
communities (Chow-Fraser 1998). Increased sedimentation also may functionally shift the fish 
community from generalist feeding and spawning guilds to more bottom-oriented, silt tolerant 
fishes (Berkman and Rabeni 1987, Muncy et al. 1979). 

Thus suspended and bedded sediments are expected to have two major avenues of effect in 
aquatic systems: 1) direct effects on biota and 2) direct effects on physical habitat, which result in 
indirect effects on biota. Some examples of direct effects on biota include suppression of 
submersed macrophytes through reduced light attenuation, changes in benthic algal communities, 
and shifts to turbidity-tolerant fish communities. Effects of suspended and bedded sediments on 
habitat structure include changes in refligia for biota (e.g., changes in macrophyte communities), 
increased fines (and embeddedness) and scouring in streams, aggradation and destabilization of 
stream channels, and filling in of wetlands and other receiving waters (Wilcock 1998, Lisle 1982, 
Dietrich et al. 1989). Increased turbidity and concomitant changes in light regime could also be 


66 


considered to be aspects of altered habitat. Indirect effects on biota will occur as the fish, 
invertebrates, algae, amphibians, and birds that rely upon aquatic habitat for reproduction, 
feeding, and cover are adversely affected by habitat loss or degradation (Platts et al. 1983, 
Hawkins et al. 1983, Rinne 1988). 

Sea grasses and other SAV are considered “keystone” species in temperate and tropical coastal 
areas. These flora have a variety of beneficial attributes including providing food and shelter for 
many biotic species. There has been a worldwide decline in sea grasses including dramatic 
regional losses in the Gulf of Mexico. The reasons for the decline are unknown but reduction in 
light attenuation (quantity and spectral quality) is thought to be a major factor. The presence of 
suspended sediments is one factor that can impact water clarity; however, its significance to this 
effect and observed sea grass declines is relatively unknown. 

Ultimately, resolution of any problems associated with increases in suspended and bedded 
sediments will need to address the sources of the sediment. These influxes of sediments, in 
general, are associated with increased sediment delivery via soil erosion often caused by changes 
in landuse and landcover, and changes in flow regimes that effect in-channel sediment transport 
and loading. Recognition of the proximal and distal “causes” of suspended and bedded sediment 
problems will affect the classification scheme used and the development of the “expectation” for 
natural sediment loads. 

Goals 

The primary goal for NHEERL’s Suspended and Bedded Sediment program is to provide and 
demonstrate the approach for establishing sediment criteria that support aquatic life use in 
streams/rivers, lakes/reservoirs, wetlands, and estuaries. A necessary first step for achieving this 
goal is to assess the current knowledge and to report on the state of the science of this research 
area. A specific APG and APM for this assessment follows. Additional goals are proposed, but 
will depend on results from both EMAP research and the literature review. 

APG 1 FY02 Synthesize state of the science and remaining uncertainties for developing criteria 
for susp^ded and bedded sediments. 

APM 1A FY02 Report on the state of the science and progress for developing suspended 
and bedded sediment criteria (AED, MED). 

Provide summary of biological response profiles for suspended and bedded sediments in marine 
and freshwater systems. 

Develop models that predict and scale biological responses to suspended solids and sediment 
using assessment endpoints that support management decisions. 

Develop classification schemes to optimize efficiency in developing suspended and bedded 
sediment criteria. 


67 




Provide the scientific basis for suspended and bedded sediment criteria for marine and freshwater 
systems. 

Critical Path 

A general diagram for NHEERL’s research program on suspended and bedded sediments is 
shown in Figure 7. An explanation of the figure is given below, including the logical sequence 
of information needed to develop the technical basis for deriving a criterion for suspended and 
bedded sediments. 

The major NHEERL responsibilities in Figure 7, under Goal 2, are to: review the literature and 
existing State criteria and develop a general conceptual model of sediment effects, including 
verbal descriptions of suspended and bedded sediment criteria that are explicit enough to shape 
subsequent quantitative modeling (box 0). Based on regional-scale data (box la,b), develop 
ecoregionally-specific models of the effects of suspended and bedded sediments on aquatic 
assemblages in various types of aquatic ecosystems (box 2); then explore and confirm 
mechanisms of sediment effects on assemblages and ecosystems through experimental research 
incorporating controlled conditions and restricted taxa at smaller scales (box Ic). Using these 
stressor-response relationships and models as a technical basis, develop an approach for 
establishing sediment criteria (box 3), which OW, EPA Regions, and States may use to establish 
criteria for suspended and bedded sediment (box 5). Using data from regional-scale surveys 
(e.g., EMAP, Regional EMAP [REMAP], National Water Quality Assessment [NAWQA)]) and 
more focused watershed studies, examine the stressor-response relationships between natural and 
anthropogenic controls and the levels and transport of sediment (box 8). Based on this research 
and models of the effects of sediments on aquatic biota (box 2), identify critical thresholds of 
anthropogenic disturbance that lead to biologically-relevant sediment responses (box 4). These 
ecoregionally-specific thresholds will be useful guidance to the TMDL process carried on by 
States (box 6). Finally, review and revise the stressor-response models and recommended 
sediment criteria based on feedback from monitoring data and further research (box 7). 

The first step in this process is a review of the literature on the biological effects of suspended 
and bedded sediments. The Office of Water has already begun this process. NHEERL can 
contribute to this effort and expand it to evaluate the literature for useable stressor/response 
relationships. Once the available literature has been reviewed, it will be necessary to develop a 
conceptual model or framework of the effects of suspended and bedded sediments in aquatic 
environments (box 0). This will guide the development of the remainder of our research 
activities. 

Classification of the expected response of aquatic systems to suspended solids and bedded 
sediments is a critical early phase of research. The first step in developing a classification 
scheme for sediments and suspended solids is the division of the national landscape into different 
eco-regions that are sufficiently fine-scaled to accurately represent the vegetation, climate, 
geology, and soils (box la). These factors will influence both the quantity and type of sediment 
and suspended solids that will be carried from the landscape into the receiving water body. The 


68 



Figure 7. Critical path for suspended and bedded sediments research. 


kinds of soils within these landscape delineations are particularly important. Secondly, 
fragmentation, storage, and hydrogeomorphic characteristics of streams and rivers need to be 
examined and classified at the watershed level as these factors influence the degree of flashiness 
of streams and rivers to precipitation, snowfall run-off, and groundwater inputs (Leopold et al. 
1964, Morisawa 1968, Mackin 1948). Ultimately these affect the timing of loadings, and 
quantity and type of sediment, not only to streams and rivers but to the receiving bodies into 
which they empty. The third step is classification by ecosystem type such as streams, rivers, 
coastal wetlands, estuaries, or near-shore zones. 

A discussion of the approaches anticipated for establishing stressor-response relationships for 
streams and rivers serves as a useful guide to the type of research anticipated in box lb. For 
bedded sediments in streams and rivers, it is likely that the expected levels of bedded fine 
sediment in relatively undisturbed streams are ecoregionally specific, depending upon natural 
climatic factors, topography, lithology, soil, and potential natural vegetation. It is also likely that 
the intensity of the response of sediment to anthropogenic disturbance will also be dependent 


69 


















































upon similar factors. The optimum regional classification to underlie modeling of sediment 
expectations and response to anthropogenic disturbance will be based on work described in box 
la. Using a combination of empirical data from relatively undisturbed watersheds and models 
describing the physics of sediment supply and transport, we will estimate expected levels of 
bedded sediment fines and embeddedness in stream and river reaches of specific size, slope, and 
location. We will then examine the association between watershed/riparian land use and the 
deviation of sediment concentrations from expected values, using survey data and data from 
more detailed watershed studies. This effort should include interaction with NERL and NRMRL 
to link landuse and landscape processes that may be responsible for delivering sediments. We 
will need to take into account the likelihood that, because of natural disturbances (fire, 
landslides, in-channel scouring due to instream hydrologic modifications), a certain portion of 
the stream or river resource may have fine sediments substantially above or below the mean 
expected value for the region. Therefore, the degree of impairment associated with deviations of 
sediment from expected values is likely to be expressed in terms of statistical probabilities. 

Once the degree of sedimentation is estimated for sample sites, we will examine associations 
between biotic assemblages (algae, macroinvertebrates, fish, rooted aquatic plants), and/or key 
aquatic species or guilds and deviations of sediment from expected values. In most cases, our 
data sets will include sites affected by multiple stressors besides sediment that could potentially 
act upon these aquatic biota. In such cases, a regional plot of sediment concentration versus 
some biotic assemblage characteristic (e.g., % EPT [Emphemeroptera, Plecoptera, Trichoptera]), 
will appear as a wedge-shaped pattern of points, where progressively higher fine sediment 
concentrations are sufficient to limit % EPT numbers, but low concentrations do not guarantee 
abundant EPT because of other habitat or chemical limitations (Terrell et al. 1996). These 
patterns are consistent with a hypothesis that sediment is limiting biota. After demonstration of 
a plausible causal mechanism (from detailed experimental studies) and elimination of other 
plausible explanations for these observations, we will use these kinds of associational data in a 
weight-of-evidence approach to support modeling the effects of bedded sediments on aquatic 
biota. 

For suspended sediments in streams and rivers, we will focus initially on chronic levels of 
suspended sediments, rather than those resulting from episodic events such as those 
accompanying storms. Expected natural levels of background suspended solids will be set on the 
basis of data from flowing waters in basins relatively undisturbed by human land uses and (in 
rivers) historic water clarity data to the extent possible. Regional reference areas could serve this 
purpose. Where no relatively undisturbed waters exist, as for large rivers, we will use historic 
data or reconstructions of fish and/or macroinvertebrate assemblage composition to infer (from 
published tolerance information) pre-disturbance suspended sediment characteristics. In an 
approach similar to that for bedded sediments, we will examine associations between biotic 
and/or key aquatic species or guilds and deviations of sediment from expected values in 
appropriate regional settings. As for bedded sediments, we will seek patterns that are consistent 
with biotic limitation by suspended sediment in a weight-of-evidence approach to support 
modeling the effects of bedded sediments on aquatic biota, supporting this information with 
controlled experimentation or literature reference to establish the suspended sediment levels that 
cause substantial impairment of assemblages, sensitive guilds, or key species. 


70 


A conditional probability approach also will be explored to determine possible effects of 
suspended sediments on the biotic condition of streams. Data from the ORD/EMAP Mid- 
Atlantic Highlands Assessment (MAHA) streams program (EMAP indicators and design), 
reported in the MAHA streams report (EPA 2000), will be used in this application. The 
approach uses survey data (sites selected with a probability design) and determines the likelihood 
of impaired biological conditions for varying threshold levels of exposure or stressor variable(s) 
(in this case, some form of suspended sediment concentration, possibly normalized for an 
expectation level). The use of survey data permits an unbiased extrapolation of results to the 
statistical population that the probability sample was drawn from. For example, the results 
would be applicable to all of the wadeable streams in a state if the sample was drawn from all 
wadeable streams in the state. 

This approach is different from typical association approaches that relate exposure or stressor 
conditions with impaired biological conditions, for example, water quality levels associated with 
impaired fish communities (fish IBI values less than 3). The approach here “stratifies” the 
resource for exceedance of a specified exposure or stressor value and then determines the fraction 
of that strata with impaired biological conditions. Since the sites were selected with a probability 
design, the fraction of the resource that is impaired is the probability of observing impaired 
biological conditions in the resource for exceedance of the threshold value. This stratification is 
then done for all values of the exposure or stressor variable. The result is a relationship for 
probability of impaired biological conditions for exceedance of the exposure or stressor values. 
This result is not a cumulative distribution function of the biological conditions since it relates 
the conditions to a threshold level of another variable, and it is more than a simple scatter 
diagram of biological condition with the exposure or stressor variable (the resource is 
incrementally integrated or summed for second variable). 

Issues associated with suspended and bedded sediments may be approached in a slightly different 
manner in estuaries. One of the primary research needs is to determine whether sea grass decline 
is correlated with the presence of increased suspended sediments. A combination of laboratory 
and field work derived under natural (box lb) and controlled conditions (box Ic) is needed to 
derive protective water clarity criteria or to set management goals to maintain existing sea grass 
coverage and community composition. This would be accomplished by the collection of 
descriptive data (mapping and field data) at a variety of sampling sites. This research would 
include monitoring basic characteristics of sea grass communities in reference areas and areas 
which historically receive high levels of suspended solids using fixed transects or experimental 
plots. Response parameters would include, but not be limited to, photosynthetic activity, 
standing crop, root/shoot ratios, epiphytic coverage, blade characteristics, sea grass cover, and 
density. Extensive water and sediment quality monitoring would be combined with this effort. 

There are a variety of experimental designs available to determine, under controlled conditions, 
the effects of suspended and bedded sediments on sea grass and other important submerged 
aquatic vegetation. The determination of sensitive species, sensitive response parameters, and 
modifying environmental factors are the objectives of these studies. The experimental designs 
include exposing different species in laboratory tests to different levels and types of suspended 
solids alone and in combination with other factors (such as salinity and nutrients) to determine 
effects on light reduction and accompanying effects on biomass, pigment content, and other 


71 



structural characteristics. Following these tests and the derivation of the necessary information, 
mesocosms would be used in the laboratory and field (enclosures) to determine effects on 
populations and communities of seagrasses. The results of the single species, population, and 
community exposures will need field validation, which box lb addresses in part. Following 
these experiments, mesocosms could be used to determine effects on populations, and 
communities of seagrasses and associated biota, particularly relating the role of seagrass beds as 
habitat to fish and shellfish populations and communities. 

The technical information on stressor/response relationships can be used to generate thresholds 
for sediment effects (box 4). These thresholds can be used in the development of criteria (box 4) 
for suspended and bedded sediments. These thresholds can serve as input to both the 
development of criteria (box 5) and the TMDL process (box 6). These criteria will be 
“integrated” criteria, similar to those discussed in the Toxic Chemical Section (Section 7). They 
will consider the effects of suspended and bedded sediments in a more holistic manner than the 
standard criteria do, taking into account effects on both benthic and water colunm organisms, and 
direct as well as indirect effects on aquatic life use. These criteria also will have to take 
magnitude, duration, and frequency of changes in suspended and bedded sediments into account. 

The availability of sediment criteria and thresholds for sediment effects will allow for a TMDL 
process that is “effects based”. The current TMDL methodologies focus much more on exposure 
and reduction of exposure, than on effects. Acceptable levels of suspended and bedded sediment 
can not presently be based on effects, because the models and stressor-response relationships to 
be derived in boxes 1 and 2 are not currently available. All that the NHEERL effort can provide, 
at this time, is information on effects on certain organisms and classes of organisms. Resource 
managers will have to use that information to make management decisions. At the same time, 
we will have to make sure that the data are provided in such a way that is useful in the context of 
designated uses. The data will have to be presented in as general a form as possible, as opposed 
to just presenting a list of data on individual species for specific magnitudes and durations of 
elevated suspended and bedded sediment concentrations. Priority will be given to those species 
which are tied to designated uses. 

Once TMDLs are produced, their effectiveness will be assessed (box 7) and this will allow 
further refinement of the models developed in boxes 0,1, and 2. 

The final piece of the TMDL process is the exposure component. Loading estimates and models 
are currently being developed by NERL, OW, and NHEERL (under Goal 8) (box 8). These 
loading estimates and models will allow estimation of the changes in suspended and bedded 
sediments inputs which might be needed to reach the targets set on the basis of the effects of 
suspended and bedded sediments effects (boxes 4 and 5). These loading estimates and models 
also provide input to the conceptual model, setting the bounds of bedded sediment and suspended 
solids inputs to aquatic systems. 


72 


Research Projects 

Project Title 1. State of Science Review 

Project Coordination and Resources (0.4 FTEs: AED-0.3, MED-0.1) 

Objectives 

To efficiently plan and manage NHEERL research efforts, a necessary first step is to assess the 
current knowledge. The objective of this effort is to fulfill the first Goal under this research 
implementation plan, which is to report on the state of the science and progress in developing 
suspended and bedded sediment criteria. 

Scientific Approach 

NHEERL personnel will summarize and synthesize current knowledge on quantitative stressor- 
response relationships among sedimentation, biota, and habitat, and the efforts to date in 
developing sediment criteria. The overall review will be conducted in concert with other ORD 
Laboratories, as well as OW, USGS, and the Army Corps of Engineers. Subject areas to be 
reviewed will include: 

1. A review of relationships between potential classification variables and suspended and bedded 
sediments. Classification variables will include ecoregional factors (e.g., vegetation, climate, 
geology, and soils); landscape characteristics such as forest fragmentation, water storage and 
hydrogeomorphology; and ecosystem type (streams, rivers, reservoirs, coastal wetlands, estuaries 
and near-shore zones). 

2. A review of the known ecological effects of suspended and bedded sediments. Effects 
categories include effects on different biotic assemblages such as microbes, primary producers, 
invertebrates, and fish; issues of scale (e.g., effects at population, community, and ecosystem 
levels of biological organization); and effects on habitat quality and quantity. Direct effects on 
biota will be contrasted with effects on physical habitat which may result in indirect effects on 
biota. Mechanistic, experimental approaches toward detecting and analyzing effects will be 
compared to large-scale empirical, correlational analyses. 

3. The effects of elevated suspended solids (turbidity) and excessive bedded sediments (i.e., 
increased sedimentation). It is expected that the ecological effects of suspended solids differ 
from those of sedimented solids. The review will encompass both stressor types. Potential 
impacts due to lack of enough suspended and bedded sediment, which can contribute to habitat 
loss, also will be investigated. 

4. Routes and mechanisms of the delivery of sediments to aquatic ecosystems. A critical step 
toward effects prediction and ecosystem protection is to understand the relationships among 
characteristics and processes at the landscape scale and the quantity and quality of sediment 
delivered to aquatic systems, whether that be terrestrial runoff with increased sediment loads or 
hydrologic modifications that result in increases in-inchannel erosion. 


73 


5. Approaches toward modeling the effects of suspended solids and sediments on aquatic 
ecosystems. 

6. Progress to date in developing state. National, and international suspended and bedded 
sediment criteria. 

Products 

APG 1 FY02 Synthesize state of the science and remaining uncertainties for developing criteria 
for suspended and bedded sediments. 

APM 1A FY02 Report on the state of the science and progress for developing suspended 
and bedded sediment criteria (AED, MED). 

Benefits of Products 

An up-to-date summary of the state of the science review of suspended and bedded sediments 
information will be provided with recommendations of needed research to develop and validate 
suspended and bedded sediment criteria for OW. 

Proposed Research Projects 

Based on results from EMAP research and the literature review, the following general projects 
are proposed assuming resources are available: 

Stressor-Response Relationships 

Associations between biotic assemblages and/or key aquatic species or guilds and deviations of 
sediment from expected values should be examined. In most cases, our data sets will include 
sites affected by multiple stressors besides sediment that could potentially act upon these aquatic 
biota. After demonstration of a plausible causal mechanism (from detailed experimental studies) 
and elimination of other plausible explanations for these observations, we will use these kinds of 
associational data in a weight-of-evidence approach to support modeling the effects of suspended 
and bedded sediments on aquatic biota. 

Thresholds for Sediment Effects 

The technical information on stressor/response relationships can be used to generate thresholds 
for sediment effects. These thresholds can be used in the development of criteria for suspended 
and bedded sediments, and thresholds can serve as input to both the development of criteria and 
to the TMDL process. They will consider the effects of suspended and bedded sediments in a 
more holistic manner than the standard criteria do, taking into account effects on both benthic 
and water column organisms, and direct as well as indirect effects on aquatic life use. 


74 


Classification 


The purpose of the classification research is to develop an effective scheme for defining those 
waters for which similar ambient levels of suspended or bedded sediments are expected. We 
anticipated the final solution will incorporate information about water body type, geographic 
setting, and specific, local hydrologic settings. This proposed classification scheme that results 
should be review with or compared to the classification in use (at that time) for establishing 
reference conditions for biological criteria. 

Gap Analysis 

Research into the problems associated with suspended and bedded sediments also is being 
carried out by other groups. The Army Corps of Engineers is working on the effects of 
resuspension associated with dredging projects, for example (Wilber and Clarke 2001). Goal 8 
research (Kaufmann et al. 1999, Kaufmann and Robison 1998, EPA 2000), research supported by 
the Office of Wetlands, Oceans, and Watersheds (OWOW) (TMDL framework for clean 
sediments), and NERL are currently contributing to the development of loading estimates and 
models (Figure 7, box 8). However, the work has barely begun on the effects of sediments to 
aquatic systems, at the low levels that may exert long-term, chronic effects. The first step 
outlined in the critical path is the review of the state of science. The intent of this gap section is 
to describe what work would remain to reach the goals once NHEERL completed the work 
outlined. The dilemma is that NHEERL will not be in a position to outline the details of what we 
will do until we complete the state of science review. At that point and in concert with Division 
management decisions on FTE dedicated to this project, the gaps that will remain can be 
identified. 

References 

Berkman, H.E., Rabeni, C. F. 1987. Effect of siltation on stream fish communities. Environ. Biol 
Fish. 18:285-294. 

Chow-Fraser, P. 1998. A conceptual ecological model to aid restoration of Cootes Paradise 
Marsh, a degraded coastal wetland of Lake Ontario, Canada. Wetland Ecol Manag. 6:43-57. 

Dietrich, W.E., Kirchner, J.W., Ikeda, H., Iseya, F. 1989. Sediment supply and the development 
of the coarse surface layer in gravel bed rivers. Nature 340:215-217. 

EPA. 2000. Mid-Atlantic highlands streams assessment. EPA/903/R-00/015. U.S. Environmental 
Protection Agency. Region 3. Philadelphia, PA. 364 pp. 

Hawkins, C.P., Murphy, M.L., Anderson, N.J. 1983. Density of fish and salamanders in relation 
to riparian canopy and physical habitat in streams of the northwestern United States. Can. J. 

Fish. Aquat. Sci. 40:1173-1186. 

Kaufmann, P.R., Robison, E.G. 1998. Physical habitat assessment. In Klemm, D.J., Lazorchak, 
J.M., eds.. Environmental Monitoring and Assessment Program 1994 Pilot Field Operations 


75 



Manual for Streams. EPA/620/R-94/004. EPA, Environ. Monk. Syst. Lab., Office of Research 
and Development, Cincinnati, OH, pp. 6-1 to 6-38. 

Kaufmann, P.R., Levine, P., Robison, E.G., Seeliger, C., Peck, D. 1999. Quantifying physical 
habitat in wadeable streams. EPA 620/R-99/003. EPA, Environmental Monitoring and 
Assessment Program, Corvallis, OR. 

Leopold, L.B., Wolman, M.G., Miller, J.P. 1964. Fluvial Processes in Geomorphology. W.H. 
Freeman and Company, San Francisco, CA, 522 pp. 

Lisle, T.E. 1982. Effects of aggradation and degradation on riffle-pool morphology in natural 
gravel channels, northwestern California. Water. Resour. Res. 18:1643-1651. 

Mackin, J.H. 1948. Concept of the graded river. Geol. Soc. Am. Bull. 59:463-512. 

Morisawa, M. 1968. Streams, Their Dynamics and Morphology. McGraw-Hill, New York. 175 

pp. 


Muncy, R.J., Atchison, G.J., Bulkley, R.V., Menzel, B.W., Perry, L.G., Summerfelt, R.C. 1979. 
Effects of suspended solids and sediment on reproduction and early life of warm water fishes: a 
review. EPA 600/3-79-042. EPA, Washington, DC. 

Platts, W.S., Megahan, W.F., Minshall, G.W. 1983. Methods for evaluating stream, riparian and 
biotic conditions. Gen. Tech. Rep. INT-138, U.S. Forest Service, Intermountain Forest and 
Range Experiment Station, Ogden, UT. 70 pp. 

Rinne, J. 1988. Effects of livestock grazing exclosure on aquatic macroinvertebrates in a 
montane stream. New Mexico. Great Basin Nat. 48:146-153. 

Simons, D.B., Senturk, F. 1977. Sediment transport technology. Water Resources Publications, 
Fort Collins, CO. 807 pp. 

Terrell, J.W., Cade, B.S., Carpenter, J., Thompson, J.M.. 1996. Modeling stream fish habitat 
limitations from wedge-shaped patterns of variation in standing stock. Trans. Am. Fish. Soc. 
125:104-117. 

Waters, T.F. 1995. Sediment in streams: sources, biological effects, and controls. American 
Fisheries Society, Bethesda, MD. 

Wilber, D.H., D.G. Clarke. 2001. Biological effects of suspended sediments: a review of the 
suspended sediment impacts on fish and shellfish with relation to dredging activities in estuaries. 
N. Am. J. Fish. Manage. 21:855-875. 

Wilcock, P.R. 1988. Two-fraction model of initial sediment motion in gravel-bed rivers. Science 
280:410-412. 


76 


Section 7. 

Implementation Plan for Toxic Chemicals Research 


Problem 

Effective management of toxic chemicals in aquatic ecosystems requires a capability to 
quantitatively predict the ecological effects that can be expected from different levels of chemical 
contamination of water, sediments, and food chains. Procedures for deriving aquatic life WQC 
have existed for many years (EPA 1973, 1980, 1991, 1994, 1995a; Stephan et al. 1985) and have 
been useful for managing toxic chemical inputs to aquatic systems. However, these procedures 
are based on simplifying assumptions and a relatively narrow framework that limit their use in 
fully assessing the risk of a wide range of toxic chemicals to both aquatic life and aquatic- 
dependent wildlife. Sediment guidelines developed more recently (EPA 2000a,b,c,d) have many 
of the same limitations as WQC. To address some of these concerns, NHEERL has prepared a 
draft wildlife research strategy for assessing risks of multiple stressors to populations of 
amphibians, birds, and mammals (EPA 2000e). 

Criteria derivation and application require extrapolations of toxicological effects observed in the 
laboratory to field conditions, which can result in significant uncertainties. Differences in water 
characteristics, chemical partitioning, routes of exposure, organism habits, and exposure time- 
series can greatly affect the relationship between exposure concentrations and a chemical’s 
toxicity, and thus affect the applicability of criteria to natural ecosystems. This is particularly 
true for PBTs, for which effects often depend on tissue residues accumulated in tissues over long 
times as a result of multiple exposure routes. Moreover, criteria often do not address the 
combined effects of multiple chemicals and other stressors, and can lack information for 
potentially sensitive life stages of test species. 

Other uncertainties in criteria arise from the use of organismal-level toxicity to set concentrations 
protective of aquatic populations and communities. The relationship of toxic effects on 
individual organisms to population responses is not well established. Important taxa and 
endpoints can be missing from the sets of tests used to develop criteria and sediment guidelines. 
Indirect effects of chemicals on organisms (effect on food sources, competition, predation, and 
shelter) generally are not considered. 

Criteria are also limited in that they address only specific water concentrations, rather than 
complete dose-response relationships, thus limiting how well risks can be characterized. 

Seasonal issues and the significance of the spatial extent of exposures are incompletely 
addressed. Current criteria procedures also do not include uncertainty analyses or address how 
well risk can be assessed in the presence of limited data. 

Because of these limitations, efforts are needed to develop methods to better characterize risks to 
aquatic life and aquatic-dependent wildlife populations and communities, and to apply these risks 
to criteria development. Assessment endpoints should be better defined and an analytical 
firework developed for linking available data to a more complete and accurate description of 
risks for these endpoints. This assessment framework should describe a range of responses, be 


77 


tiered to allow some decisions to be made with limited data, and should include analysis of the 
uncertainties in estimated risks. 

Goals 

The general goal of this work is to develop scientifically-defensible methods for better describing 
the risks of toxic chemicals to aquatic and aquatic-dependent populations and communities, in 
support of improving criteria procedures needed to satisfy GPRA Goal 2, Objective 2. The work 
to be discussed here represents only part of the ecological toxicology efforts within NHEERL 
and is focused on prospective assessments of chemicals for which WQC exist or are desired. As 
such, the proposed research will not address issues which are not closely related to such 
assessments and/or are a subject of research under other goals. This includes such areas as 
chemicals and endpoints of emerging or potential concern, biological indicators (including DNA 
or protein-based probes) for retrospective assessments and diagnostics, and basic investigations 
of the cellular/subcellular and physiological mechanisms of toxicological responses. 

Further details on research needs and NHEERL research efforts to address those needs are 
described in the following subsections. Specific goals of these efforts are summarized in the 
following APGs and APMs Note: some APGs and APMs (including those under GPRA) were 
established before this document was written. They are listed here along with APGs and APMs 
that were developed under the aquatic stressors process. 

APG 1 FY02 (GPRA #31) Provide a method for setting risk-based aquatic life criteria for toxic 
chemicals which minimizes uncertainties of translating national and site-specific water quality 
criteria. 

APM 1A FY02 (GRPA # 167) Report on integrated water and sediment quality criteria 
methods for assessing site-specific risks of persistent bioaccumulative toxicants to 
aquatic species (MED). 

APG 2 FY03 Demonstrate methods to set risk-based water quality criteria for toxic substances. 

APM 2A FY03 Describe a framework for WQC for nonbioaccumulative chemicals that 
more fully describes risk to aquatic organisms (MED). 

APG 3 FY05 (GPRA #111) Provide methods for developing WQC based on characterization of 
population-level risks of toxic chemicals to aquatic life and aquatic-dependent wildlife. 

APM 3A FY04 (GPRA # 59) Population models that project the relative risks of multiple 
stressors (toxic chemicals, habitat alterations) to piscivorous birds (AED, MED). 

APG 4 FY06 Provide methods for extrapolating chemical toxicity data across exposure 
conditions and across endpoints, life stages, and species, which can support assessment of risks 
to aquatic life and aquatic-dependent wildlife for chemicals with limited data. 


78 


APM 4A FY02 Interspecies correlation estimations (ICEs) for acute toxicity to aquatic 
organisms (GED). 

APM 4B FY02 Time-concentration-effect models for use in predicting chronic toxicity 
from acute toxicity data (GED). 

APM 4C FY03 Acute-to-chronic estimation (ACE) user guide and software (GED). 

APM 4D FY06 Report evaluating importance of dietary route of exposures to aquatic risk 
assessments for metals (MED). 

APG 5 FY08 Provide approaches for evaluating the relative and cumulative risks from toxic 
chemicals, with respect to risks from nonchemical stressors, on populations of aquatic life and 
aquatic-dependent wildlife at various spatial scales. 

APM 5 A FY05 Report regarding assessment of risks to aquatic organisms from 
combined exposure to polycyclic aromatic hydrocarbon (PAHs) mixtures and ultraviolet 
(UV) radiation in natural systems (MED). 

APM 5B FY06 Approaches for addressing spatial scale issues in assessing risks of 
multiple stressors to wildlife populations in spatially-diverse landscapes (AED, MED). 

Critical Path 

Defining critical research paths needed to improve aquatic risk assessments and criteria 
development for toxic chemicals should start with consideration of the problem formulation that 
should be part of any good risk assessment (Figure 8). There needs to be clear definition of the 
assessment problem, including the ecological effects (assessment endpoints) and exposure 
scenarios of concern, and better conceptual models which define the logical structure of the 
assessments. These conceptual models should identify critical toxicological endpoints to be used 
in the assessment (i.e., measurement endpoints) and how these are to be related to the assessment 
endpoints, based on knowledge of the dynamics of the ecosystem(s) of concern. There also 
should be specification of how assessments might be tiered initially, basing evaluations on 
limited data to determine whether risks might be significant, and adding data as needed to make 
more definitive assessments. 

With better definition of the conceptual model, the needs of the other phases of a risk assessment 
can be better identified. Methods are needed so that the exposure profiles (Figure 8) can 
describe, in sufficient detail, the distribution of the toxicant(s) relative to the biological receptors. 
This would include evaluation of the temporal and spatial variability of exposure and the 
chemical’s speciation and partitioning to the extent needed to determine the distribution of 
toxicological responses. Response profiles (Figure 8) need to provide good organism-level 
response models and linkages between organism response and population/community responses. 
When limited toxicological data are available, methods for extrapolating among species and 
endpoints also will be needed. Methods must support risk characterizations which describe a 


79 


Mijf.'iit; F,v<^li!ation of (:• ii cu'venKfs and Cntnpliancfi 


P^ob^^^rr rormu=dt:n 


Exposure 
Sources & 
Pathways 


Ecological 


Critical 

E fleets of 


Species and 

Concern 


Endpoints 




Population & 
Community 
Dynamics 


T oxicant 


Conceptu al 


E cosystem 

Properties 


Model 


Properties 




Loading 
M agnitude 

ST 



Loading 

Distribution 


S 


.... 


Fate and 


Speciation, 


Receptor 


T ransport 


Partitioning 


Distribution 

< >• ••••«•>•••« •• •• . .. 

.. ... 




^Analysis 


Toxicological and Ecological 
Data 


Organism 

Response 


Population 

Dynamics 


Spatially- 

distributed 

Response 





Exposure Profile 


Response Profile 


(Temporal^patial/Form Distribution) 


(As Function of Exposure Distribution) 




Figure 8. Ecological risk assessment framework (modified from EPA 1992). 


80 














































range of effects, including their uncertainties, and sufficiently account for important site 
characteristics. The resultant risk descriptions should allow risk managers to assess the 
implications of different exposure scenarios and, once acceptable risks are specified, to back- 
calculate acceptable loadings (dotted line on Figure 8). 

Critical research paths also should recognize the need to implement progressive improvements in 
the assessment methodology. Figure 9 shows four steps in such improvement, which largely 
parallel the APGs described in the preceding Goals subsection. The first step will demonstrate 
how criteria can reflect more comprehensive risk characterizations, based on more complete 
descriptions of organism-level dose-response relationships and how they vary with exposure 
conditions. The second step will involve the development and application of methods to address 
population-level risks. The third step will involve the development of extrapolation methods so 
that the risks can be assessed for chemicals or situations for which limited data is available. The 
fourth step will include further enhancement of these techniques and emphasize their 
incorporation into multistressor assessments at various spatial scales. 


Demonstrate methods for improved criteria 
at the individual level based on 
improved characterization of risks 


- 4 ^ 

Develop methods to link individual-level data 
to population-level endpoints 


4 . 

Develop methods to support risk assessments 
for chemicals with limited data 


4 . 


Develop methods to evaluate risks on 
populations at various spatial scales, 
in the context of other stressors 


Figure 9. Critical path for developing site-specific methodologies for establishing the risks of 
toxic chemicals to aquatic life and aquatic dependent wildlife. 


81 







The rest of this section will further discuss the improvements needed in aquatic risk assessment 
methods and identify the research and development efforts needed to realize these improvements. 
This will be done in the context of conceptual models which identify the essential components of 
assessments and emphasize the link of this research to the risk assessment framework. The 
nature of the assessments and the needed research can vary depending on the chemical and 
system of concern. One issue of particular importance is the degree to which chemicals 
bioaccumulate. Risk assessments for highly bioaccumulative toxicants will differ in some key 
features from assessment for chemicals which have low bioaccumulation. For bioaccumulative 
toxicants, dietary exposure will be especially important, residue-based dose-response models will 
be more important, and bioaccumulation models will be needed for dose determinations. 
Toxicokinetics for these chemicals are generally slow, so acute toxicity and short-term temporal 
variation in exposures will often, but not always, be of less importance than for 
nonbioaccumulative toxicants. Biomagnification makes risk relatively more important for 
organisms high in the food chain, and some risk assessments of bioaccumulative chemicals could 
concentrate on populations of such organisms, rather than on the broad aquatic community. 
Because of these differences, the assessment needs will be discussed separately below for 
nonbioaccumulative and bioaccumulative toxicants. 

It is true that assessments of these two groups of chemicals will share many common features and 
principles, and thus these separate discussions will involve some redundancies. For example, 
toxic response for almost all chemicals and endpoints is ultimately related to the amount of 
chemical which accumulates at an internal site of action, so a residue-based framework is of 
value to nonbioaccumulative chemicals as well as bioaccumulative chemicals. Also, the 
consequences to populations and communities of toxic effects on individual organisms will entail 
the use of similar tools for both groups of chemicals. Furthermore, whether a chemical is 
"bioaccumulative" is a matter of degree, not a discrete category. Nonetheless, the relative 
importance of various issues will vary between assessments of chemicals with low versus high 
bioaccumulation. Separate discussions are useful in highlighting how assessments will vary, and 
in identifying needed work. However, the results of the work proposed below will often 
transcend the chemical group and efforts will be made to apply any results as broadly as is 
appropriate. 

Nonbioaccumulative Toxicants 

Nonbioaccumulative toxicants include a wide range of organic and inorganic chemicals that are 
of concern in many aquatic risk assessments. Notable examples include heavy cationic metals 
(such as copper, zinc, lead, cadmium, and silver), which are significant contaminants in mining 
areas and various effluents; and ammonia, which is at high concentrations in sewage effluents, in 
some fertilizer runoff, and in areas with high inputs of nitrogen-containing organic compounds. 
These chemicals have been documented to have substantial impacts on certain aquatic systems 
and are responsible for many instances of noncompliance with WQC. Treatment and 
remediation costs associated with meeting criteria for these chemicals can be high. However, as 
discussed above, current assessment and criteria methodology entail various uncertainties 
regarding the actual risks of these chemicals and thus whether the regulatory controls are 
necessary and sufficient. 


82 


Cationic metals and ammonia will be used as examples, where helpful, in the discussion here and 
as candidates for chemical-specific research discussed later. They represent major concerns in 
current aquatic risk assessments and have a rich toxicity literature that will support development 
of dose-response models, and evaluation of the quality of assessments made with limited data. 
However, while the discussion here will in part highlight these chemicals, the issues discussed 
are true for many nonbioaccumulative toxicants. General methods developed for any particular 
chemical should be directly, or by example, applicable to other nonbioaccumulative toxicants. 
Conceptual Model 

As discussed above, research needs for development of risk assessment methods need to relate to 
the conceptual model for the assessment(s). Figure 10 presents a simple conceptual model that 
identifies important elements that need to be considered in conducting and developing methods 
for aquatic risk assessments and criteria development. The horizontal series of boxes on the 
bottom of the figure represent major stages in linking chemical stressors to ecological responses. 
Loadings of toxic chemicals to an aquatic system are distributed throughout the system, resulting 
in exposures to biological receptors. These exposures may adversely affect the survival, growth, 
and reproduction of individual organisms. Such individual-level effects may be expressed at the 
population level as changes in population size, growth rate, and structure. Changes at the 
population level may in turn elicit changes in the community. The "stacked-boxes" used to 
represent these stages are intended to indicate that assessments often will involve multiple 
chemicals and/or biological species. 

The arrows linking the lower series of boxes represent mechanistic relations among these stages, 
and involve the application of various models identified by the series of ovals in the center of the 
figure. The arrows are bi-directional to indicate the fundamental similarities between two modes 
of environmental protection: 1) criteria development and application that determine chemical 
loadings consistent with protecting desired system values (left-to-right) and 2) assessments that 
characterize risks expected from chemical exposures (right-to-left). 

The boxes on the top of Figure 10 identify data needs for the models used in the assessment. All 
of the models will generally require various information on various physical, chemical, and 
biological characteristics of the system. The exposure and toxicity models also will require 
information on the nature of the chemical and its toxicity to various biological receptors. 

This conceptual model helps to identify and evaluate research needed to improve aquatic risk 
assessments and criteria development. Primary attention should be given to the four basic 
models in Figure 10. How are current assessments limited by the inability of exposure models to 
completely and accurately predict and describe the relationship between chemical loadings and 
exposure concentrations? How well do current methods actually describe risks to individual- 
level endpoints under exposure conditions expected in natural systems? What is the significance 
of organismal-level effects to populations and communities? Another research focus would be 
the adequacy of data needed for these models. Can required chemical properties be estimated 
well? Does available toxicity data address the endpoints needed in the assessment? 


83 




84 


Figure 10. Simple conceptual model for risk assessments of nonbioaccumulative toxicants. 

















How well can toxicity data be extrapolated among species and endpoints? Finally, research 
efforts should address the need to describe and test a complete methodology for conducting 
assessments and developing criteria. 

Research Needs 

Per the conceptual model presented in Figure 10, fundamental tools of assessments are the 
models linking the state variables. Brief descriptions of each model and its research needs are as 
follows: 

Exposure Model 

The exposure model should produce a description of the spatial/temporal distribution and the 
speciation of the chemical that satisfies the input needs of the effects models. It must also 
describe other physical and chemical properties that can affect toxicological re^nse. Because 
NHEERL research primarily addresses effects assessment, only a brief overview of exposure 
research needs will be given here. These do, however, represent significant knowledge gaps that 
need to be addressed if improved risk-based criteria are to be implemented. 

The exposure model must assess physical transport, degradation, speciation, and partitioning of 
the chemical. Current implementations of WQC and WQS often use simple models that will not 
support more comprehensive characterizations of risks. For physical transport, the primary need 
is to adapt and apply better methods and models that are available. In particular, better risk 
characterizations need dynamic, two- and three-dimensional models, which can more fully 
describe the spectrum of exposures experienced by biological receptors. 

Degradation is not an issue for elemental toxicants such as metals, but is of utmost important for 
ammonia. Decomposition of nitrogen-containing organic matter produces ammcxiia, while 
nitrification in oxygenated water reduces ammonia concentrations. Nitrification is reduced at 
colder temperatures, resulting in higher and more widespread ammonia concentrations during 
winter months. More comprehensive characterization of risk will require more complete 
modeling of the spatial and seasonal distribution of ammonia. Of particular concern is the 
ammonia concentrations in or near the sediment that results from the interaction of organic 
matter decomposition, nitrification, and mixing processes. 

Speciation is well characterized for ammonia, but current models for metal speciation have 
considerable uncertainty, especially regarding complexation by organic matter. There is a need 
both to improve current predictive models and also to develop analytical techniques that can 
reliably and efficiently measure metal speciation in laboratory tests and natural systems. 

Whether by model or measurement, these methods must describe speciation sufficiently for the 
interpretation and application of the effects of various chemical parameters on metals toxicity. 

Partitioning between sediment and overlying water is of utmost importance to assessing the risk 
of both ammonia and metals. Exposure analyses for the application of WQC need to incorporate 
the role of sediment as a source or sink of chemical, processes that entail considerable 
uncertainty. Currently there are no published EPA guidelines for ammonia in sediment, but 


85 


Equilibrium-partitioning Sediment Guidelines (ESGs) for metals (EPA 2000a) use acid volatile 
sulfide (AVS) and interstitial water to predict biological effects. These methods have been 
demonstrated to be very useful in predicting biological effects in laboratory experiments and in a 
limited number of field experiments, but few of them have examined the importance of temporal 
and spatial variability in exposures within and near sediments, which limits the usefulness of 
these equilibrium approaches. Research is needed to characterize how AVS and metals 
concentrations at the sediment/water interface vary seasonally and due to hydrological events to 
support better characterization of effects. 

Toxicity Model 

The toxicity model in a risk assessment must integrate exposure information, toxicological data, 
interspecies extrapolations, dose-response nK>dels, and effects of exposure conditions into 
characterizations of risks for various organism-level endpoints, species, and life stages. 

Depending on the requirements of the assessment, including the tier level, various research and 
development needs exist. 

A significant shortcoming in current criteria and many assessments for nonbioaccumulative 
toxicants is consideration of toxicity information for only a single level of effect at a set exposure 
duration (e.g., 96-hr LC50). Critical to better risk characterization are models that more 
comprehensively describe toxic response, including both the magnitude of response and the 
effect of exposure time series. Some capabilities for this have long existed. Models that describe 
the relationship of effects to concentration or time are standard tools in toxicity, but traditionally 
have not combined the effects of both. Furthermore, these models generally assume that 
concentrations are constant. Mancini (1983) described a model that would describe the effects of 
fluctuating concentrations based on standard toxicity tests, but did not incorporate variable levels 
of effects into the model. Subsequent work has tested the utility of this approach with mixed 
results and broadened the model to describe variable levels of effects as a function of both 
concentration and time. Although this model does involve significant uncertainties, it is well 
suited to form the core element in risk assessments and criteria development. It can use toxicity 
information from standard tests and describe the level of effects, with uncertainties, expected 
from any exposure series. This model can thus be combined with expected spatial and temporal 
exposure distributions to produce individual-level risk curves for populations of biological 
receptors. This would provide the individual-level risk characterization in Figure 10 needed for 
any further risk characterization at the population or community level. Other modeling 
approaches, such as proportional hazard and accelerated failure models, also might provide a 
basis for better describing toxicological risks and their dependence on exposure time-series. 

Based on current information, the critical immediate need is to further develop these techniques, 
establish their validity and uncertainty, and describe their application to WQC, which would be a 
major part of the first step in Figure 9. This would establish a core framework for improving 
criteria, but also would identify knowledge gaps that should be addressed by further research. 

Chemical toxicity to aquatic organisms can be influenced markedly by various physicochemical 
exposure factors. For example, ammonia toxicity can vary by orders of magnitude due to the 
combined effects of pH and temperature and by significant amounts due to DO and certain major 
ions. EPA aquatic life criteria for ammonia (EPA 1999) are a function of pH and temperature. 


86 


but do not fully account for these effects and do not account at all for other factors. An 
especially important uncertainty (which is also relevant to other chemicals) is the effect of winter 
conditions on chronic ammonia toxicity to life-stages of sensitive organisms present during the 
winter. Aquatic life WQC for metals currently are expressed as dissolved metals, with a 
correction for hardness in fresh water, reflecting empirical observations of reduced toxicity due 
to adsorption by suspended solids and increased hardness. Research has shown, however, that 
several factors other than hardness also are important in determining the toxicity of metals in 
water, and has identified mechanistic bases for their effects. Development of a model that 
incorporates many of these factors, referred to as the biotic ligand model (BLM) is currently 
being supported in part by OW. Applications of current versions of this model to criteria entail 
substantial uncertainties, especially for chronic toxicity and for certain taxonomic groups. 
Significant research in support of this model is underway under the support of OW and various 
industry groups. Such efforts will contribute to the third step in Figure 9. 

As discussed above regarding exposure research issues, organisms dwelling in or on sediments 
can receive chemical exposures with large temporal and spatial variation, due to hydrologic 
events disturbing the sediments or seasonal changes affecting partitioning or processing of 
chemicals. Decomposition of nitrogen-bearing organic matter in the sediment can cause 
substantial gradients between sediment and overlying water. For metals, sediment can be 
relatively more contaminated than overlying water due to past inputs, diagenesis of deposited 
particles, or seasonal fluctuations in loads to systems, leading also to substantial gradients from 
sediment to overlying water that are subject to marked variation. Sediment pore water metal 
concentrations in anoxic layers are greatly affected by AVS, which varies with season and depth 
and can be oxidized when sediment is resuspended. Even in laboratory tests, sediments and 
overlying water are likely at some disequilibrium that varies with time and between tests. 
Regulations based on assumptions of equilibrium therefore can involve considerable uncertainty. 
The toxicological implications of non-equilibrium conditions (such as resuspension and 
seasonality) need to be investigated as part of the extrapolation methods represented in the third 
step in Figure 9. 

Laboratory tests used for aquatic risk assessments of nonbioaccumulative toxicants typically 
include exposures only via chemical dissolved in water, the organisms either not being fed or fed 
food that is not contaminated commensurate with the water concentrations. This is not an issue 
with ammonia, as ammonia per se would not be a significant contaminant in food, although 
catabolism of food is a source of ammonia that organisms must eliminate. However, the 
significance of the dietary route of exposure is an important issue for metals assessments. 
Aquatic animals certainly can accumulate substantial amounts of metals from their diets, and 
diets contaminated with high concentrations of metals can cause adverse effects. However, the 
importance of this route of exposure when there is also commensurate amounts of metal 
dissolved in the water is uncertain. Some studies have presented evidence that dietary uptake 
results in increased uptake and/or risk compared to water exposure alone, while other studies 
have suggested the opposite. The uncertainty regarding the significance of dietary uptake to 
metal risk is a fundamental barrier to good risk assessments for these chemicals and could have 
substantial implications to regulatory programs. 


87 


A common uncertainty in any aquatic risk assessment is the sensitivity of species and endpoints 
not directly tested. In some cases, there is considerable toxicity data, but certain important data 
are missing, for example, having acute mortality data, but not chronic reproduction data, for a 
critical or sensitive species. Additionally, there are many cases where desired assessments have 
available only a limited number of laboratory toxicity data, if any. Therefore, there is a need for 
models that address extrapolations across endpoints and/or species, and the uncertainties of these 
extrapolations. This relates to the toxicity data needs box in Figure 10 and is a major part of the 
third step in Figure 9. A research effort is needed to improve and test extrapolation methods, and 
to apply these methods to simulated risk assessments with limited data, providing better 
methodology for conducting interspecies extrapolations for nonbioaccumulative toxicants in 
general. 

A final area of major concern is the joint effects of multiple chemical stressors and the effects of 
chemicals in the presence of non-chemical stressors. Much laboratoiy-based research has been 
done on these issues, but has seen little application to EPA criteria, except recently for sediment 
toxicity assessments. A principal need is to determine how this past work should be incorporated 
into assessments, and to better identify what additional work will be worthwhile. 

Population Model 

Toxicity tests can provide information on a diverse set of endpoints, but comparing the relative 
risk of these endpoints and their significance at the population and community level is difficult. 
An important step in better defining the significance of these toxicological effects is to 
incorporate them into population model that will translate these effects into some common 
’’currency” of population dynamics. A critical research need therefore is to develop, test, and 
apply population models for a variety of species relevant to assessments of nonbioaccumulative 
toxicants. This is represented in the second step in Figure 9. These models ^\^ll be used not only 
to describe the significance of observed and predicted organism-level toxicity, but also to 
evaluate the usefulness of toxicity tests and to determine needed changes in the types of tests 
conducted. 

Stage-structured population models can be used to link individual-level effects to the population 
level, such that a set of vital rates defines the dynamics of a population. Such models require 
estimates of vital rates for all life stages, which may or may not show effects from a given level 
of toxic chemical exposure. A set of vital rates also can define the life history strategy of a 
species. Life history strategies vary along a continuum from species with early reproduction, 
high fecundity, and short life expectancy (r-selection model) to species with delayed 
reproduction, low fecundity, and long life expectancy (K-selection model). Population-level 
responses may differ between species because of differences in life history strategies, even 
though individuals of different species may show a similar response to a toxic contaminant. 
Variation in life history strategies, and therefore variation in population-level responses to toxic 
contaminants, also may occur geographically in the same species. 

Population models should be constructed for fish, shellfish, and wildlife species exhibiting 
different life history strategies (i.e., species exhibiting different sets of vital rates). Population 
models generic to groups of species whose life history strategies are similar also can be 


88 


developed for use at the screening level or tier of risk assessments, to be followed by models 
specific to individual species in hi^er-tiered assessments. 

An important issue regarding population models is the type of individual-level effects they need 
as inputs to provide useful assessments of population-level effects. The toxicity tests and data 
typically available are often limited with regard to the endpoints and life-stages tested, often 
leaving significant gaps in the information needed to assess impacts of toxicity on population 
dynamics. Population model development needs to identify the critical inputs and be coordinated 
with toxicity model research to provide the methodology to make this information available. In 
particular, efforts to improve extrapolation of toxicity data should take such needs into 
consideration. 

Community Model 

Unless a^ssments are simply intended to provide protection to the most sensitive taxonomic 
groups, some synthesis is needed to relate expected effects on populations or individuals to 
consequences to the aquatic community. As mentioned above, current WQC are premised on the 
judgment that occasional low level toxicity to sensitive species constitutes accqjtably low risk to 
those species and to the aquatic communities to which they belong. This might be a sound 
judgment, but the lack of quantification makes it impossible to conclude whether higher 
exposures might also pose minimal risk, or what acceptably low risk means. 

There is thus a need to better quantify the association between toxic effects and effects on aquatic 
communities. This could entail a few lines of efforts. Better meta-analysis is needed of the 
variety of community assessments in experimental or natural ecosystems, linking these to a 
metric that better describes available toxicity data. More efforts are needed to study responses to 
toxic chemicals in complex aquatic ecosystems. Aquatic community models could be developed 
that integrate effects at the individual or populations-level. As part of these efforts, there is also a 
need to identify individual- and population-level endpoints important for supporting better 
community-level assessments, and to coordinate with toxicological research to provide this 
necessary information. 

Bioaccumulative Toxicants 

Toxicity risks associated with PBTs are expected to be advantageously assessed with residue- 
based dose response models. This important concept may be used to describe the minimum 
degree to which chemicals must be persistent and bioaccumulative in order to fall into this class. 
The degree of persistence determines what concentrations in water and sediments are available 
for incorporation of the chemical into benthic and pelagic food chains which lead to exposure of 
vulnerable organisms. The potential for a chemical to bioaccumulate in an organism is 
commonly referenced to concentrations of the chemical which persist in water and sediments. 
Thus, bioaccumulation factors (BAFs) and biota sediment accumulation fectors (BSAFs), in 
accordance with mechanistic food chain models which integrate all routes of exposure, are 
extremely important components of the risk assessment methodology for PBTs. For organic 
chemicals, hydrophobicity, as measured by the octanol-water partition coefficient is the 
primary determinant of bioaccumulation potential with metabolism in the food chain as an 


89 


important factor for decreased bioaccumulation potential. Bioaccumulation for other PBTs, such 
as organometallic compounds like methyl mercury or organic chemicals with mechanisms for 
bioaccumulation different than hydrophobicity, must be assessed on the basis of the mechanism 
for their bioaccumulation. 

The first residue-based WQC for PBTs (PCBs, DDT and metabolites, mercury, and 2,3,7,8- 
tetrachlorodibenzo-p-dioxin [TCDD]), were developed for the Great Lakes under the Great Lakes 
Water Quality Guidance (EPA 1995a). Criteria were developed for risks to human health and 
wildlife, but not aquatic life. A similar procedure with BAFs was used to promulgate the 
National methodology for deriving human health criteria (EPA 2000f). 

Conceptual Model 

The Great Lakes Water Quality Guidance (EPA 1995a) for PBTs is consistent with a conceptual 
model for risk based criteria development for determination of safe loadings to aquatic systems 
or remedial actions in cases where unsafe loadings have created contaminated sediment 
problems. This conceptual model (Figure 11) illustrates a number of fectors that are relevant to 
development of risk assessment methods and water quality criteria for PBTs in aquatic 
ecosystems. The double arrows represent models and relationships for transforming and linking 
the data and conditions rejx^sented by the boxes in the conceptual model. Concepts therein 
which provide an initial basis for development of projects for aquatic PBT research are: 

1. Wildlife, aquatic life, and human health risk assessment methodologies for PBTs follow 
parallel tracks with common elements such as toxicity models, bioaccumulation factors, and 
chemical fate and transport models. 

2. Residue-based WQC for PBTs should incorporate all of the elements of the risk assessment 
paradigm (problem formulation, effects/hazards analysis, exposure analysis, risk characterization, 
and risk management). Setting water and sediment quality standards, in accordance with the 
PBT conceptual model (Figure 11), requires a left to right logic for acquiring data and 
assembling models. 

3. Retrospective risk assessments (e.g., what are/were the ecological risks associated with the 
mass of chemical X in lake Y?) most often occur in response to a chemical stressor diagnosis and 
utilize the models and data in a right to left direction. 

4. While prospective and retrospective risk assessments tend to flow from left to right and from 
right to left, respectively (Figure 11), in reality, site sjjecific assessments are expected to involve 
iterations with reverse flows in data colleaion and modeling. For example, in a prospective risk 
assessment differences in exposure for species in a particular ecosystem may alter assumptions of 
the species at risk and require extrapolation of toxicity data (return to effects analysis) to 
complete a risk characterization. 


90 




(0 

£ 

3 

(O 

o 

Q. 

X 

LU 


Ui 

•O 


N 

(0 


I 


(T 



91 


bioaccumulative toxicants to aquatic systems. 





































































































5. To date, PBT risk assessment methods for wildlife and humans have been based on 
assessment of dietary exposures involving concentrations in aquatic food organisms, principally 
fish and shellfish. Although this results in common BAFs/BSAFs for aquatic life and wildlife, 
BAFs/BSAFs specific for wildlife species are measurable and could be modeled to reduce 
uncertainties associated with the present dietary exposure methodology. 

6. Determination of sensitive species, critical end points for population sustainability, and 
residue-based toxicology data are largely generic considerations which can be assembled on a 
national basis. However, bioaccumulation, exposure conditions, and chemical mass balance are 
more site-specific considerations. 

7. The conceptual model applies to deterministic or probabilistic analyses. 

8. Toxicity risks are linked to changes in chemical loadings or remedial actions and vice versa, 
so that tiered assessments are possible with choice, in the problem formulation, of steady state as 
either a specific assessment condition or as a reference condition for time-dependent risk 
assessment. 

9. Spatially explicit risk assessments require spatially explicit exposure and bioaccumulation 
analysis with feedback (right to left) to population model responses through the residue based 
toxicology model (Figure 11). Spatially explicit risk analysis for PBTs can be conceptualized, in 
accordance with Figure 11, as multiple fish or wildlife tracks based on different exposure levels, 
a common toxicity model, and the potential for different population level responses (assuming 
discrete populations, or meta-populations exposed within the assessment region). 

10. The third dimension in the conceptual model (stacked boxes) may be viewed as the multi¬ 
stressor component, particularly for chemical mixtures. Each chemical has to satisfy the risk 
assessment requirements in a parallel manner with a joint toxicity model (such as toxicity 
equivalence) linking each chemical’s contribution to the net exposure. 

Research Needs 

For this discussion, the five different paths in Figure 11 (box-arrow-box components) that 
involve the generation and use of data to accomplish components of risk assessments will be 
examined for research needs. Separate but parallel chains of the five paths emerge for aquatic 
and wildlife species from the conceptual model for PBTs. The five components are: 1) the 
relationships between chemical loadings to the ecosystem and exposures via food, water, and 
sediment; 2) the relationships between concentrations in food, water, and sediment to 
concentrations in tissues of organisms at risk; 3) the relationships between concentrations in 
organisms and incidence of effects; 4) the relationships between incidence of effects and 
population changes; and 5) the relationships between specific population changes and community 
structure and function. The chemical loading, fate and transport, and exposure path (mass 
balance model) is not a direct NHEERL research concern but must be consideral as a reference 
point for integration of effects research with exposure research. 


92 


Bioaccumulation Model 


Much progress has been made in the last decade toward providing a reliable bioaccumulation 
prediction capability for aquatic organisms through complementaiy use of empirical BAFs and 
more mechanistic food chain models. Site-specific variations in bioavailabilhy have been largely 
reduced through lipid and organic carbon normalization and tropic level determination. Besides 
the chemical’s hydrophobicity and potential for metabolism in the food chain, we now recognize 
the distribution between water and sediments and the relative benthic versus pelagic food web 
composition as critical determinants of bioaccumulation. The following important gaps need to 
be filled: 

1. Rates of metabolism are needed for many PBTs in order to allow accurate predictions of 
bioaccumulation with food chain models. These rates are best determined from field data in 
order to fit the risk assessment needs. 

2. The metabolism rate gap extends to bioaccumulation of PBTs like the PAHs in embryo-larval 
stages of fi^ with potential vulnerability to photo-induced toxicity. 

3. Very few bioaccumulation data sets are of sufficient quality to validate the uses of BAFs and 
BSAFs, especially when extrapolated across species and/or ecosystems. 

4. Existing BAFs, BSAFs, and food chain models are based on whole adult organisms and thus 
may not be sufficient when dose to early life stages (ELSs) and/or specific tissues must be 
evaluated. Early life stage dosimetry-based bioaccumulation factors and PB-TK models are 
needed to fill this gap. 

5. Comprehensive and compatible BAFs, BSAFs, and food chain models are needed to meet the 
requirements of joint action toxicity models such as for TCDD toxicity equivalence or photo- 
induced PAH toxicity. 

Toxicity Model 

As would be expected, existing toxicity data vary greatly in amount and applicability for different 
PBTs. The risk assessment paradigm (Figure 8) and the conceptual model for PBTs (Figure 11) 
provide contexts for determining toxicological research needs for PBTs. Chemical residue-based 
dosimetry is an essential requirement. Ideally, residue dose-response models should relate to the 
most sensitive end points, species, and life stages which may result in population declines. 

Often, however, the toxicity data available are obtained prior to establishment of specific risk 
assessment requirements. This increases the need for development of tools for extrapolation of 
effects across species and end points. It also increases the need to plan future toxicology research 
so that the data fit into an ecological risk assessment profile for the particular class of PBT. Such 
a profile might be based on the mechanism of toxicity, ecological and exposure vulnerability of 
species, and expectations for differences in species sensitivity. A trivial example would be the 
low benefit to be expected from investigation of a specific receptor mediated toxicity in a class of 
aquatic organisms which is known to not possess the receptor. Most PBTs fall in groups based 


93 


on a common mechanism of toxicity and thus joint toxicity models are an important facet of this 
path. The following important gaps need to be filled: 

1. Residue-based toxicity data bases need to be advanced and evaluated for applicability to 
aquatic ecological risk assessment requirements for PBTs. 

2. Is absence of overt mortality, even for early life stages, an adequate effects end point for 
preventing population declines caused by PBTs? If not, how do we determine what is adequate? 

3. Commonly measured biochemical effect indicators, such as P450 enzyme induction, have 
uncertain relationships to organismal, much less population-level, risks. 

4. Complex, multi-stressor models, such as required for photo-induced PAH toxicity to fish 
during embryo-larval stage of development, need to be developed and applied to determine the 
magnitude of ecological risks which are presently highly uncertain. 

5. Virtually unexplored are the toxicokinetic and toxicodynamic determinants for interspecies 
and inter-effect extrapolations of potency ratios required for PBT mixture toxicity risk 
assessment using a toxic units model approach, such as the additive TCDD toxicity equivalence 
model. 

Population Model 

Population models are used to translate organismal responses to toxicity into population changes 
which may reflect risk to the population. Thus, life stage specific mortality or chronic effects 
which reduce survival may lead to a reduced population or extinction. While population models, 
such as the Leslie matrix age-class or individually based models, have been developed, relatively 
few applications in retrospective risk assessments for aquatic organisms exposed to PBTs have 
been reported. Examples of prospective use of population models for protection of aquatic life 
from PBT toxicity are fewer. This is unfortunate because WQC and other forms of risk 
assessment for protection of aquatic life from PBT toxicity should have a problem formulation 
based on the levels of population protection required. Clearly, the population model path is 
important for relating toxicity-induced mortality of individual organisms to population level 
changes in an ecosystem. However, a potentially equally important use is for the systematic 
definition of species characteristics, life stages, and toxicity effects that are most likely to 
determine risks to populations associated with PBTs having a particular mechanism of action. 
The following important gaps need to be filled: 

1. Population models need to be developed and applied through case studies to explicitly 
demonstrate risk assessment requirements for prediction of adverse population impacts, as a 
result of PBT toxicity caused reductions in survival of aquatic organisms. 

2. Complex mixtures of PBTs are the norm, so interspecies differences in potency, as well as in 
bioaccumulation, for individual chemicals in the mixture must be factored into population level 
risk predictions. 


94 




i 


3. Because PBTs tend to distribute widely, if not uniformly, in aquatic habitats, uncertainty 
exists for the extent to which spatially explicit population models are requisite for problem 
formulations. 

4. A national WQC methodology for different classes of PBTs needs definition, through use of a 
generic population model, of the species characteristics, life stages, and toxicity effects that are 
most likely to determine risks to populations, regardless of site conditions. This information will 
provide the basis for determining site-specific model and data requirements for application of the 
criteria. 

Research Projects 

The research and development needs presented in the critical path constitute a very large effort 
encompassing integrated assessment methodology, generic assessment components applicable to 
many chemicals, and data or model needs for specific chemicals. Formulating a Goal 2 toxic 
chemicals research program for NHEERL requires consideration of what can be done with 
available resources to produce the most beneficial improvements in assessment methodology 
over the next several years, while also considering what research will be done in other goals and 
by other parties. The research presented here was selected to provide both general methods 
development and reduction of specific uncertainties. 

Overvietv of Projects-Nonbioaccumulative Toxicants 

At the most general level, there is a need to describe the overall framework and methods that 
should be used to improve aquatic risk characterizations and criteria development/application. 
This includes a short-term need, as represented by the initial step in Figure 9, to describe general 
approaches for improving assessments for nonbioaccumulative toxicants and the use of currently- 
available methods and knowledge, thus providing a starting point for criteria development and 
further research. There also is a longer-term need to periodically update the recommended 
framework and methodologies as research in various areas produces useful results. Project N1 
{Improved Risk Characterization Methods for Developing Aquatic Life Criteria for 
Nonbioaccumulative Toxicants) will address these needs and serve as a focal point for 
developing research projects, and applying and integrating their results. Initial products from this 
project will address APG 2, but efforts will continue under this project to synthesize results from 
other research, describe their application to assessments, and address, in whole or part, other 
APGs. This continuing work will be in large part in collaboration with OW efforts to revise their 
guidelines for WQC derivation. The nature and time line of additional products will be defined 
as these collaborative efforts develop during FY03. 

As discussed in the Critical Path subsection, more meaningful aquatic risk assessments require 
improved approaches and knowledge in various areas. Prominent among these are models that 
address the significance of toxicological responses at the population-level (APG 3). Projects that 
we will pursue in this area will only involve bioaccumulative toxicants (see below), but the 
results of such work will have relevance to nonbioaccumulative toxicants and will be part of 
continuing efforts in project Nl. Proposed research projects involving nonbioaccumulative 
toxicants involve the general topic area identified in APG 4 (extrapolation of toxicity among 


95 



exposure conditions and biological endpoints). This encompasses many important issues, and 
three specific areas were selected which address major uncertainties or gaps for criteria, which 
are feasible with available resources, and which are not being resolved to our knowledge in other 
research programs. 

Methods which can extrapolate toxicity data across different endpoints, species, and life-stages 
can benefit criteria development and application in two major ways. First, the importance of 
endpoints or organisms missing from toxicological data sets used for criteria development can be 
estimated (e.g., endangered species). Second, criteria for some chemicals could be developed 
from more limited data sets than generally required. Project N2 {Methods for Extrapolating 
Chemical Toxicity Data Across Endpoints, Life Stages, and Species Which Can Sipport 
Assessment of Risks to Aquatic Life for Chemicals with Limited Data) will address certain 
methods for conducting such extrapolations. 

The toxicities of some contaminants are particularly sensitive to exposure conditions, sometimes 
varying by orders of magnitude. Even with a better risk assessment framework and other tools, 
good risk assessments of such contaminants are not possible without resolution of the effects of 
various exposure parameters on toxicity. A particularly noteworthy uncertainty is the 
bioavailability of metals. The Office of Water is cumently supporting development of BLM, 
which addresses the effects of various chemical constituents on metal toxicity and is based in part 
on previous work in this area by NHEERL. Because of these efforts and various industry-funded 
research projects, further work on water-borne metal bioavailability is not being proposed here. 
Rather, work is proposed in areas that represent critical knowledge gaps that are receiving less 
attention. 

Toxicological responses of organisms in or on sediment are affected by temporal and spatial 
variations in chemical concentrations and speciation in the sediment/water boundary zone. This 
is true in laboratory test systems and even more so under field conditions, making the 
interpretation and application of toxicity data highly uncertain. This area of concern will be 
addressed in project N3 {Assessing the Significance of Non-equilibrium Conditions on Aquatic 
Guidelines to Better Predict Field Effects). (Note : Because of resource reductions after the initial 
preparation of this plan, this project will not be pursued at this time.) 

Aquatic life criteria for metals have generally assumed that exposure is predominantly via water, 
with dietary exposure being negligible; however, some work has indicated that this assumption is 
not true and that risk might be substantially underestimated. Project N4 {Risks of Heavy Metals 
to Aquatic Organisms from Dietary Exposures) will address this area of uncertainty for metals 
risk assessment and should also provide results and insights that can help address this issue for 
other nonbioaccumulative toxicants. 

Overview of Projects-Bioaccumulative Toxicants 

The residue-based toxicity approach for PBTs embodied in the conceptual model for ecological 
risk assessments and criteria development (Figure 11) is the foundation for advancing methods 
that will effectively link PBT loadings to aquatic ecosystems, or watersheds to risks for adverse 
population changes in aquatic life and wildlife associated with aquatic food webs. Therefore, 


96 



there is a need to establish a residue-based toxicity framework with associated models. This 
framework is intended to improve aquatic ecological risk assessments and criteria 
development/application. In so doing, it can also be used to design, conduct, and report results 
for a long term PBT aquatic risk research program. The framework, like the framework for non- 
bioaccumulative toxicants, should be upxlated periodically and revised as new research improves 
and expands the methods, models, and data available for effectively applying the conceptual 
model. These jjeriodic improvements need to be reported to OW and other interested Program 
Offices in a context that emphasizes integration into the framework and practical applications of 
the resulting risk assessment methodology. Project B1 {Framework for Development and 
Application of Population Risk-Based Criteria for Fish and Wildlife Exposed to Persistent 
Bioaccumulative Toxicants) serves this need. The initial products under project B1 will address 
APG 1. Subsequent improvements to the framework and validation efforts will concentrate on 
population level impacts and thus address APG 3. As for project Nl, work in this project will in 
large part be in conjunction with OW efforts to update W(^ guidelines. The nature and time 
line of additional products will be defined as these collaborative efforts develop during FY03. 

As discussed in the Research Needs subsection for bioaccumulative toxicants, BAFs and models 
are essential for application of the residue-based toxicity approach. Although basic models and 
approaches are available for predicting bioaccumulation throughout aquatic food webs, improved 
capabilities are needed. Prime examples are the need to incorporate the effects of chemical 
metabolism into predictions of whole organism chemical elimination rates, the need to predict 
site-specific bioaccumulation with minimum data sets, and the need to extend bioaccumulation 
models to allow tissue residue predictions for vertebrates during the embryo and subsequent early 
stages of development. Project B2 {Incorporate Chemical Metabolism Rates and Site-specific 
Bioavailability into Bioaccumulation Models Structuredfor Practical Assessments of Risks to 
Fish and Wildlife Exposed to PBTs) is intended to advance the state of knowledge and risk 
assessment capabilities for all five of the major gaps identified under the Bioaccumulation Model 
Path. In so doing, project B2 will ultimately share with project B4 a goal of development of a 
model for fish ELS bioaccumulation of chemicals with significant metabolism potential such as 
PAHs. Although influencing APG 3 and APG 5, project B2 will primarily address APG 4. 

(Note : Because of resource reductions after the initial preparation of this plan, this project will 
not be pursued at this time.) 

Projects B3 and B4, although focused on specific PBTs, will provide products needed to fill 
important gaps associated with both the toxicity model and the population model paths of the 
conceptual model. Exposure to methyl mercury arguably creates the most widespread and 
intractable PBT risk problem for piscivorous birds. Project B3 {Multiple Stressor Risks to 
Common Loon and Other Piscivorous Bird Populations) will provide population models that are 
capable of evaluating relative risks of multiple stressors, including habitat alterations, in response 
to APG 3. Project B3 will also advance methods for interspecies extrapolation of dose-response 
relationships through development of PBTK/TD models in support of APG 4, and extend the 
population model to assessment of risks to wildlife from multiple stressors across spatially 
diverse landscapes in support of APG 5. Project B4 {Risks to Fish Populations from PAHs in 
Natural Systems) inherently addresses complex chemical mixture modeling issues with 
complexity added in association with the need to model photo-activation of PAHs in tissues of 
organisms, including early life stages of fish. Since data will probably always be limited for 


97 




assessing photo-induced PAH toxicity and thus extrapolations are required to assess risks, project 
B4 will contribute to APG 4. Project B4 also will provide the key aquatic life related 
contribution to APG 5. The determination of the extent to which populations of aquatic 
organisms, including fish during early life stages, are reduced due to photo-induced toxicity 
across the broad range of PAH contamination in aquatic ecosystems will require achievement 
and application of a highly advanced PBT risk assessment capability. This PBT risk problem 
will provide a uniquely challenging test of PBT risk assessment methodologies because, although 
concentrations of complex mixtures of PAHs in organisms may be sufficiently described as a 
steady-state condition, the timing of UV exposure required for photo-activation is highly variable 
in time and space as well as being subject to habitat conditions and organismal/species behavior. 

Project Title NL Improved Risk Characterization Methods for Developing Aquatic Life 
Criteria for Nonbioaccumulative Toxicants 

Project Coordination and Resources (4.0 FTEs: AED-1.5, GED-0.5, MED-2.0) 

Objectives 

Current WQC incorporate only limited information regarding the magnitude and time- 
dependency of the responses of aquatic organisms to toxic chemicals. They address only one 
point (the fifth percentile) in the distribution of toxic effects concentrations among tested species. 
No uncertainty estimates are made and the importance of untested species and endpoints is not 
assessed. Assessments are not made at ail in the absence of certain minimum datasets. The 
spatial variation of exposure, especially between sediment and water column, is not addressed. 
Except for recent efforts regarding sediment assessments, the effects of multiple stressors are 
generally not considered nor are the consequences of toxic effects on populations as a result of 
exposure of individuals. These limitations result in a weakly defined definition of risk associated 
with criteria conditions and an inability to quantify how risk would change with exposures above 
or below criteria concentrations. 

Methods do exist for more completely addressing many of these issues, albeit with some 
uncertainty, and thus providing a more meaningful statement of risk. However, this will require 
adoption of more comprehensive risk assessment framework for criteria. A major short term 
objective of this project will be to describe a methodological fi'amewoik for such risk 
characterization that could be used to improve criteria derivation. Current and possible 
methodologies for the components of this framework will be described, identifying where 
improvements to criteria are possible with current knowledge and where research efforts are 
needed. This effort will provide APM (2A) under APG 2. 

The longer term objective of this project will be to further test, refine, and describe this 
framework and its component methodologies, based on results of research conducted in other 
projects in this program and elsewhere. In particular, we will address application of methods for 
population-level assessments and for extrapolation of toxicological information among endpoints 
and exposure conditions (APG 3 and APG 4). Products will consist of reports which synthesize 
new information and update descriptions of risk assessment methods relevant to aquatic life 
criteria. Much of this work will be pursued in collaboration with OW efforts to update WQC 


98 


guidelines, and the specific nature and time line of products will be developed as part of this 
collaboration. 

Scientific Approach 

This project will use existing and developing information to evaluate and demonstrate procedures 
for more fully characterizing risks of nonbioaccumulative toxicants to aquatic organisms, and 
incorporating these risks into aquatic life criteria These efforts will address a variety of issues, 
identified and discussed as follows. For all these issues, initial efforts will describe general 
approaches, discuss the capabilities and limitations of current methods, and identify needed 
research efforts. 

• Improved descriptions of risk must start with methods which better describe individual- 
level concentration-response relationships than endpoints that address only a single level 
of effect under narrow exposure conditions (e.g., 96-hr LC50). A chemical will be 
selected (likely candidates: ammonia, copper) for which raw toxicity test data are 
available for a large number of tests and diverse aquatic species. Existing methods for 
describing the effects of exposure time series on toxicological response (Mancini 1983, 
Breck 1988, Erickson et al. 1989, Hickie et al. 1995, Meyer et al. Newman 1995) will be 
evaluated, refined, and tested using these data sets. Models and estimation procedures for 
describing fixed-duration concentration-response curves also will be evaluated. These 
methods will be used to develop procedures for describing risks, and their uncertainties, 
for individual-level endpoints as a function of exposure parameters in a manner useful for 
criteria development and application. 

• Efforts also will address better description of species-sensitivity distributions to allow 
criteria to address more than just a single level (i.e., the fifth percentile genus) in the 
range of available toxicity data. Methods for describing the distribution of toxic 
sensitivities among taxa (Kooijman 1987, Wagner and Lokke 1991, Aldenberg and Slob 
1993, Baker et al 1994, Solomon et al. 1996, Hall et al. 1998, Newman et al. 2000) will 
be reviewed and used to develop an aggregate, continuous measure of risk from the 
assemblage of available toxicity data, which can be applied to various exposure 
conditions and provide a more quantitative basis for specifying aquatic life criteria. 
Uncertainties in this analysis will be described to the extent possible and the effects of 
using limited data on the estimated risks and their uncertainties will be evaluated. 

• The use of population models in the recently developed saltwater DO criteria will be 
reviewed and the broader applicability of population models to criteria will be addressed 
in initial efforts. Subsequent work will, as appropriate, review advances in methods from 
other research in this plan (projects B1 and B3) and elsewhere, and ^xlate 
recommendations regarding assessment methods for aquatic life criteria. 

• Correlations of responses among taxa and endpoints will be discussed with regard to how 
criteria can address data gaps and limited data. Current work on interspecies 
extrapolation in project N2 will be summarized in initial products, and results of 
continued work in this project will be included in later efforts. 


99 


• The importance of spatial variation in exposure for assessing risk to aquatic communities 
will be evaluated and discussed. This will include the general issue of the spatial extent 
of the area that WQC criteria are intended to protect. It will also include consideration of 
the effects of spatial and temporal variability at the sediment/water interface (drawing on 
results from project N3) and the implications of this to the integration of sediment and 
water column criteria. 

• The effects of physicochemical exposure conditions and of multiple routes of exposure 
will be addressed. Initial efforts will include general reviews of the importance of such 
factors and current abilities to address these effects. Later efforts will address 
developments from ongoing research regarding these effects, including results from 
project N4 on effects of dietary metals. 

• Exposure to multiple chemicals will be addressed. Existing literature which describes the 
effects of chemical mixtures will be reviewed, procedures appropriate for WQC 
development will be developed, and the significance of joint toxic action to common 
contamination scenarios will be examined. 

Products 

FY03 Journal articles evaluating methods for describing the relationship of toxic responses to 
time and concentration and their application to improved expressions of risk for WQC (part of 
APM 2A). 

Benefits of Products 

This work will provide OW with a prototype framework for criteria based on a more accurate and 
informative characterization of organismal-level risk, and will provide a more quantitative basis 
for deriving and evaluating criteria. This framework will address several of the limitations of 
current criteria and support incorporation of tools addressing other limitations, such as 
population-level effects and extrapolations among species and toxicity endpoints. Products will 
provide a technical basis for making changes to procedures for developing criteria. 

Project Title N2, Methods for Extrapolating Chemical Toxicity Data Across Endpoints, Ufe 
Stages, and Species Which Can Support Assessment of Risks to Aquatic Life for Chemicals 
with Limited Data 

Project Coordination and Resources (1.5 FTEs: GED-1.5) 

Objectives 

Ambient WQC have enabled the states to develop scientifically defensible standards and thereby 
reduce the amount of specific chemicals discharged into our nation’s surface waters. However, 
environmental managers must frequently perform risk analyses and make decisions regarding 
compounds for which WQC and the data necessary to derive them (EPA 1980) do not exist. 
Guidance is needed on comparative sensitivity relationships and on limits of extrapolation, and 


100 


uncertainty for use by managers who must on many occasions, make decisions based on the 
limited data that may be available. This is of particular concern for the protection of certain 
endangered species which cannot be tested or other species that are not feasible to test. 

Most species sensitivity comparisons have been made on individual chemicals, and modes of 
action have been used for certain extrapolations for individual species. However, there is little 
understanding of the relationships and uncertainty between chemical classes and the sensitivity of 
taxonomic groups of species to these chemical classes. A particular mode of action (chemical 
class) may pose greater or lesser jeopardy to certain families/populations of organisms than 
others, allowing one to better assess what will occur ecologically (diversity). This concept will 
be evaluated in extrapolating chemical toxicity data across species and taxonomic groupings. 

The present research specifically covers five major modes of action (see second item below), but 
will add others from existing data bases, further supporting chemical modes of action/structure- 
activity research being addressed by NHEERL/WED. 

Effects on native fish and invertebrate populations are important indicators of changes in surface 
waters due to human-related impacts such as the damming of rivers, lowering of aquifers, 
addition of pollutants, and introduction of non-native species. The planned research to determine 
the utility of using surrogate species in hazard evaluations to estimate the potential for toxic 
chemicals to affect other aquatic species has the following objectives: 

1. Assess the rainbow trout, fathead minnow, and sheepshead minnow as appropriate surrogate 
test species for endangered fishes and other species. 

2. Determine differences in acute sensitivity to chemicals with differing modes of action 
(carbaryl, copper, 4-nonylphenol, pentachlorophenol, and permethrin) between surrogate test 
species and selected endangered organisms. 

3. Develop interspecies correlations between surrogate test species and endangered fishes and 
other aquatic species using 48-h EC50/96-h LC50 data for the above five chemicals. 

4. Develop user manual and software for interspecies correlations of acute toxicity data for 
aquatic species using data bases from Mayer and Ellersieck (1986), Mayer (1987), OPP, and 
AQUatic toxicity Information REetrieval (AQUIRE). 

5. Perfomi acute toxicity tests to fill data gaps identified in item 4 where performance of specific 
tests would significantly enhance the number of data sets available for use in developing 
interspecies correlations (e.g., additional modes of action in item 2). 

6. Enhance utility of ACE (acute-to-chronic endpoint) model to predict chronic toxicity to 
endangered and other species on a population basis. 

This research will further develop project N1 by addressing the importance of untested species 
and endpoints and providing methodology for assessing limited data sets and filling data gaps. In 
addition, models for estimating chronic toxicity from acute toxicity data will concentrate on 
population-level assessments. 


101 



Scientific Approach 

Existing toxicity data bases, comparative toxicity literature, and methods for extrapolation among 
endpoints, life stages, and species of aquatic organisms will be reviewed to identify areas where 
improvements can be made in extrapolation based on existing data. 

Specific data gaps, particularly those addressing modes of action/structure-activity, will be 
identified and tests conducted where performance of laboratory toxicity tests would significantly 
enhance the ability to extrapolate among endpoints, life stages, and species. This research can 
best be accomplished by collaboration among the Ecology Divisions, because some of the data 
gaps will involve freshwater testing and others will involve testing of saltwater species. 
Collaboration with other governmental agencies such as the FWS, NOAA, and U.S. Geological 
Survey (USGS) is also desirable. 

Methods for acute tests will be based on standard methods such as Weber (1993) and ASTM 
(1988). Dissolved oxygen, pH, salinity, and temperature will be measured in all treatments on 
day 0 through day 4. Most of the effort with endangered species has been completed and any 
additional testing of such species will be conducted by USGS/BRD, Columbia Environmental 
Research Center, Columbia, MO through an interagency agreement jointly funded by ORD, OW, 
and Office of Prevention, Pesticides, and Toxic Substances (OPPTS). 

Statistical analyses will be performed on survival data with probit analysis or the Spearman- 
Karber program to generate as a minimum, 24-, 48-, 72- and 96-h EC/LCSOs. Three-way 
analysis of variance is performed using SAS to determine statistical differences between species 
LCSOs, among chemicals, and at each time interval. 

Interspecies correlations will be conducted using Model n least squares methodology for two 
independent variables (Mayer and Ellersieck 1986) on a combined data base from Gulf Breeze, 
FL, Columbia, MO (USGS), Office of Pesticide Programs (OPP), and AQUIRE. Slopes and 
intercepts are derived from the equation log y = a + b (log x), where x equals 48-h EC50/96-h 
LC50 values for the surrogate test species and y equals 48-h EC50/96-h LC50 values for the 
endangered or other species. Chemical groupings by mode of action will also be compared to 
taxonomic groupings of species to ascertain any differences in vulnerability. A user manual and 
software, based on a Windows platform, will be developed to use the data base as a reference 
catalog and for user interaction to derive calculated values based on their input data. 

For acute-to-chronic predictive models, both classical and non-classical time-to-event approaches 
(Crane et al. 2001, Jones 1964) will be used. The appropriate computer language(s) to combine 
all three acute-to-chronic models, linear regression, multifactor probit analysis, and accelerated 
life testing (Mayer et al. 1994, Lee et al. 1995, Sun et al. 1995), including life table subroutine, 
will be determined. The accelerated life testing model will be the population-based estimation 
procedure using an underlying life-time distribution/survival probability distribution (e.g., 
exponential, Weibull, extreme value, log-normal, gamma, or log-gamma models). This approach 
will define extra response above natural mortality due to chemical exposure. We will develop 
appropriate computer commands using compatible language that will allow for: 1) data 
input/transfer fi'om pre-established data bases, 2) selective and continuous processing of data 


102 


through all or selected combinations of the three models, and 3) printouts of graphics. The final 
phase is establishment of a user-fnendly Windows version of the ACE software. This will be a 
joint effort with the University of Missouri, Columbia, MO, throu^ a cooperative agreement 
funded by ORD/NHEERL, OW, and OPPTS. 

For selected classes of chemicals, the utility of estimating WQC values based on limited data will 
be examined by applying extrapolation techniques to subsets of data for existing WQC. This 
exercise will provide insights into the accuracy and margin of error that might be expected by 
such extrapolations. 

Products 

FYOl Report on sensitivity of threatened and endangered fishes to contaminants with 
comparisons to that of standard surrogate species. 

APM 4A FY02 ICEs for acute toxicity to aquatic organisms (GED). 

APM 4B FY02 Time-concentration-effect models for use in predicting chronic toxicity from 
acute toxicity data (GED). 

APM 4C FY03 Acute to chronic estimation (ACE) user guide and software (GED). 

FY06 Refinement of ICEs for acute toxicity to aquatic organisms based on new data. 

Benefits of Products 

Research products developed under this plan will provide managers with extrapolation methods 
and guidance to use when making decisions based on limited data sets. Specifically, 1) 
interspecies correlations will allow the user to estimate the acute toxicity for a species having no 
data ^m a common test species having acute data and 2) ACE allows prediction of chronic 
toxicity using only acute toxicity data. Estimates for both acute and chronic endpoint predictions 
include measures of accuracy and uncertainty. 

(Note : Because of resource reductions after the initial development of this plan, 
the following project will not be pursued at this time.) 

Project Title N3, Assessing the Significance of Non-Equilibrium Conditions on Sediment 
Guidelines to Better Predict Field Effects 

Project Coordination and Resources (3.0 FTEs: AED-3.0) 

Objectives 

The majority of U.S. EPA’s sediment guidelines are based on the assumption that equilibrium 
conditions exist (EPA 2000a,b,c,d). This assumption may be one of the largest weaknesses in 
these guidelines. In the laboratory, where many guidelines are developed, this assumption is 


103 







probably correct. However, in the field, where these guidelines are applied for regulatory 
applications, equilibrium conditions may not always be present. Examples of non-equilibrium 
conditions include seasonal changes in the benthic environment resulting in changes in A VS 
levels (Leonard et al. 1993), storm events and dredging operations under which sediments 
become suspended into the water column (Karickhoff and Morris 1985a,b, Calvo et al. 1991, 
Simpson et al. 1998, Latimer et al. 1999, Bonnet et al. 2000), and the presence of unusual 
binding phases in the water column and benthos (e.g., soot carbon) (Gustafsson and Gschwend, 
1997). The occurrence of conditions resulting in non-equilibrium may alter how well guidelines 
function and cause under-protective situations as well as introducing unacceptable uncertainty. 
Non-equilibrium, along with the importance of dietary uptake, is one of the greatest remaining 
sources of uncertainty in the development of criteria for non-bioaccumulative compounds. 

The over-all objective of this project is to perform research allowing the Agency to better 
understand the importance of non-equilibrium conditions and have greater confidence in the use 
of aquatic guidelines under field conditions. The research will focus on three predominant areas 
which may result in non-equilibrium: seasonality, sediment suspension, and unusual binding 
phases. Seasonality affects nearly every environmental setting and therefore its impacts on the 
effectiveness of aquatic guidelines must be understood. Sediment suspension occurs under a 
variety of environmental situations ranging from storms to dredging, and signifies a potential 
source of toxic chemicals to most coastal systems. Unusual binding phases are a recently 
recognized source of variability to the application of aquatic guidelines but nonetheless may 
represent a significant source of error to existing regulatory values. Another critical component 
of this research will be to complete an evaluation of how well current guidelines predict adverse 
effects in field sediments. This information will be very useful in designing studies to assess the 
effects of non-equilibrium conditions on guidelines development and application. Although this 
work focuses on non-equilibrium in the sediment, the issues explored also have consequences for 
the water column, via the sediment-water interface, and for other sources of heterogeneity in the 
sediment as well. 

Scientific Approach 

Seasonality 

Seasonality is especially important in relation to metals in sediment. Currently there are no 
published EPA guidelines for metals in sediment, but ESGs for metals are under development. 
These guidelines use A VS and interstitial water to predict biological effects. These methods 
have been demonstrated to be very useful in predicting biological effects in laboratory 
experiments and in a limited number of field experiments. The current draft guidelines do not 
explicitly consider the effects of seasonality on AVS and metal bioavailability. However, it is 
known that AVS varies with season and depth as a function of seasonality (Leonard et al. 1993); 
therefore, it is possible that metal bioavailability will correspondingly vary. If metal 
bioavailability varies significantly over an annual cycle, decisions based on the comparison of 
sediment guidelines with measured chemistry taken at one time of year may not be appropriate 
for other times of the year. Research in this section will seek to quantify the magnitude of 
fluctuation of metal bioavailability as a function of season. 


104 


Sediment Suspension 

As noted above, sediment suspension is a natural and anthropogenic process that results in the 
release of contaminants into the water column. Research has demonstrated this geochemical 
phenomena (e.g., Latimer et al. 1999, Cantwell et al. in prep.) but little study has investigated the 
biological effects of sediment suspension and contaminant release on benthic oiganisms. This 
section of the project will perform studies to assess the magnitude of contaminant release and 
effects under realistic environmental scenarios. 

Initially, resuspension and benthic flux experiments will be performed under controlled 
(laboratory) conditions in order to determine the significance of specific variables in the 
remobilization of contaminants from sediments. In this phase, toxicity tests will be perfcxmed to 
measure adverse effects. Resusp^sion of contaminated field sediments will be performed at a 
number of energy levels and time scales which are representative of estuarine conditions, with 
continuous monitoring of chemical changes to the sediment and overlying water. Additional 
experiments will take place resuspending sediments and monitoring longer term (1-6 month) 
fluxes and chemical changes to the sediment. Once a better understanding of the variables 
influencing stressor availability is gained, field-based experiments will be conducted. 

Unusual Binding Phases 

Currently, ESGs for metals and organic chemicals are based on the concentrations of the binding 
phases A VS and organic carbon, respectively, in the benthos. In recent years, it has been 
speculated and observed that other binding phases may also influence the bioavailability of 
metals and organic chemicals. For example, organic carbon has been speculated to affect metal 
bioavailability beyond the influence of AVS (DiToro et al. 2002). Further, while the 
bioavailability of organic chemicals like pesticides and PCBs are well predicted using organic 
carbon (EPA 2000b, Burgess et al. 2000), the behavior of PAHs vary widely (Gustafsson and 
Gschwend 1997). Unusual binding phases like soot carbon have been proposed to explain these 
discrepancies (Gustafsson and Gschwend 1997). 

To address the significance of unusual binding phases, research will be conducted to assess the 
relative importance of these phases as compared to AVS and organic carbon as currently applied 
by Agency guidelines. The draft ESGs for both metals and PAHs contain adjustments for 
unusual binding phases but these adjustments are crude and based on limited scientific 
information (EPA 2000a,d). Consequently, research will be performed to expand and/or improve 
the current adjustments factors to ultimately reduce the variability in regulatory guidelines these 
unusual binding phases introduce when used in the field. 

Field Validation of Aquatic Guidelines 

It is prudent to field validate the existing aquatic guidelines as this provides a way to determine 
their effectiveness. One efficient way to do this is to use the guidelines along with our increased 
understanding of the effects of contaminants in dynamic and variable sediments to predict the 
toxicity of sediments collected in large data bases for which concurrent chemistry and toxicity 
data are available, such as the EMAP and NOAA Status and Trends data bases. Numerous 


105 




attempts have been to match toxicity data to various sediment guidelines, but there are no 
published reports of a study which used all of the available guidelines to predict and explain 
toxicity in the samples. 

In this project, the sediment guidelines (i.e., which are based on the equilibrium partitioning 
[EqP] model) will be applied using a toxic unit model to determine whether concentrations of 
chemicals measured commonly in sediment monitoring programs (cationic metals, PAHs, PCBs 
and other non-PAH narcotic chemicals, and pesticides) appear sufficient to explain observed 
toxicity (EPA 2000a,b,c,d). Where they are not, it may be inferred either that unmeasured 
chemicals (e.g., ammonia) or measured chemicals not included in the toxic unit model are 
contributing to toxicity, or that the EqP model does not provide protective guidelines. Part of 
this effort will be to improve the EqP model to correct these potential errors. 

Products 

As a result of this project, a data set will be generated describing the effects of those factors 
resulting in non-equilibrium conditions including seasonality, suspended sediments, and unusual 
binding phases. This research recognizes the equilibrium assumption of existing guidelines as a 
potential weakness which may compromise their utility if not better understood. Further, we will 
perform an assessment of how effective current guidelines are for predicting field effects. 

FY02 Report to OW on the effectiveness of ESGs in the prediction of amphipod mortality in 
sediments. 

FY02 Peer-reviewed journal article on the usefulness of EqP in the prediction of amphipod 
mortality in sediments. • 

FY05 Report to OW on the importance of seasonality and resuspension in predicting the 
biological effects of contaminants in sediments. 

FY06 Report to OW on the importance of unusual binding phases in predicting the biological 
effects of contaminants in sediments . 

Benefits of Products 

Currently, the usefulness of the Agency’s sediment guidelines, which are based on EqP is 
potentially limited in regard to field application by our poor understanding of the effects of 
seasonality, resuspension, and unusual binding phases. For example, A VS has been used to 
develop ESGs for metals, but its application is limited. Better understanding of the biological 
effects of contaminants under non-equilibrium conditions would greatly increase the applicability 
of the guidelines in the field. 

The Office of Water has chosen EqP as the method for developing ESGs, but before these 
guidelines can be accepted in the scientific and regulatory community, they must receive further 
validation in the field. This research is one step in that validation. 


106 


Project Title N4, Risks of Hea^y Metals to Aquatic Organisms from Multiple Exposure 
Routes 

Project Coordination and Resources (4.0 FTEs: MED-4.0) 

Objectives 

A fundamental uncertainty with the use of typical laboratory toxicity tests in assessing risks of 
nonbioaccumulative toxicants to aquatic organisms is the failure to account for other routes of 
chemical exposure which may occur in natural systems. Of particular concern is exposure to 
chemicals via food or by incidental ingestion of contaminated non-food solids. For many 
nonbioaccumulative toxicants this failure is arguably of little consequence, but for some 
chemicals risk might be significantly underestimated using only water exposures. Conventional 
wisdom in aquatic toxicology was that water is the primary exposure route for metals (e.g., 
copper, cadmium, nickel, lead, zinc) to fish and other aquatic organisms (with the exception of 
metals such as mercury which form significant amounts of bioaccumulative oiganometallic 
species). Although exposure to metals via the diet was known to produce some level of 
bioaccumulation, it was not considered to significantly increase risk relative to water-only 
exposures, unless the diet was highly contaminated relative to that in equilibrium with water. 
Thus, environmental criteria and other toxicity assessments for metals have focused on 
waterborne toxicity, but there has been considerable concern whether this is adequate. 

Beginning in the early-1990's a series of dietary toxicity studies were conducted (Woodward et 
al. 1994, 1995; Farag et al. 1994) that involved feeding young rainbow trout diets prepared from 
invertebrates collected from metal-contaminated rivers, primarily the Clark Fork River (CFR) in 
Montana. The Clark Fork watershed is highly contaminated with several metals, with copper 
being generally considered to be the metal of greatest concern. Results of these studies showed 
that fish fed a diet of pellets prepared from metal-enriched invertebrates showed reduced growth 
relative to fish fed similar diets prepared from invertebrates fi'om reference areas, or less 
contaminated portions of the CFR. A more recent study from the same laboratory (Farag et al. 
1999) reports comparable findings for invertebrates from the Coeur d’Alene watershed in Idaho, 
where the primary metals of concern are lead and zinc. The authors of these studies conclude 
that the metals in these diets are the cause of the toxicity to rainbow trout. However, these 
conclusions conflict with previous studies which have not shown such toxicity from dietary 
metals. Additionally, Mount et al. (1994) conducted a laboratory study which fed a live diet of 
brine shrimp (Artemia) that were cultured at high metal concentrations to produce nauplii that 
were high in metal content. These studies did not indicate that dietary metals caused the degree 
of effects noted in studies with the field-collected invertebrate diet. The discrepancies among 
these studies is attributed by some to differences in the form of the metal in the diet. 

For invertebrates, similar uncertainties and conflicting results exist regarding the importance of 
the dietary route of exposure. A variety of studies have demonstrated intake of metals via 
ingestion in various molluscs and crustaceans, but the importance of this uptake relative to 
uptake via water and its significance to toxic response generally has not been well determined. In 
particular, there are questions about differences in the efficacy of metals taken up via different 
routes, about the water exposure a particular dietary exposure should be compared to, and about 


107 




how separate water and dietary exposures relate to combined exposures. However, Hook and 
Fisher (2001) have demonstrated reproduction of freshwater cladocerans and marine copepods to 
be reduced at much lower silver concentrations when exposure was via food equilibrated to 
various water concentrations, as opposed to directly to water at those same concentrations. Other 
unpublished work by Hook and Fisher (2000) has indicated the same is true for mercury, zinc, 
and cadmium. However, in similar experiments, Kim et al. (2000) have reported that 
reproduction of cladocerans was more severely affected by waterborne cadmium than by dietary 
cadmium. 

The repercussions of this issue in regulatory programs are large and persistent. The significance 
of dietary exposure has not only been a major point of contention in the Superfiind assessment of 
the CFR, but has infiltrated debates on a number of regulatory issues. These include the 
adequacy of ambient WQC for metals, to advisability of assessing waterborne metals on the basis 
of dissolved (rather than total recoverable) metal, and the adequacy of EPA’s proposed ESGs 
(formerly Sediment Quality Criteria) for metals. None of these programs currently considers 
dietary exposure to metals as part of assessing risk. Yet, at present, the technical debate is mired 
in conflicting and insufficient data with no clear resolution of the biological significance of the 
dietary pathway, much less a way of quantifying the risk for incorporation into environmental 
regulation. This project will address this fundamental uncertainty in the toxicity model for 
metals and will contribute to APG 4. 

The general objective of this project will be to assess the importance of considering routes of 
exposure other than water in aquatic risk assessments and criteria development for cationic 
metals. Initial efforts will address the importance of dietary exposure relative to water and will 
include 1) review and synthesis of past and ongoing work of other investigators to better define 
the state of knowledge regarding the importance of dietary exposure, and 2) targeted experiments 
which ccxifirm critical work and address uncertainties. 

Scientific Approach 

1. Dietary Metals Effects on Fish. 

This will include a series of experiments in which juvenile fish (rainbow trout, fathead minnows, 
channel catfish) will be fed diets of live, intact invertebrates that have been enriched with metal 
in a variety of ways, including through waterborne exposure of the prey to metals and through 
rearing prey in metal-contaminated sediment, both field-collected and artificially spiked. The 
oligochaete, Lumbriculus variegatus, has a number of attractive features as a prey species, 
including ease of mass culture and tolerance of sediment contamination, and we anticipate using 
it as a primary prey model. However, we will also conduct limited experiments with other 
invertebrates, such as the midge, since some researchers have speculated that the metal 
interaction with chitin may be involved in the observed effects. By using sediments collected 
from field locations such as the CFR and the Keweenaw Waterway (Michigan), these 
experiments can also address responses to real-world mixtures of metals in addition to 
laboratory-prepared mixtures. Exposures in initial experiments will be solely through the diet, 
minimizing exposure via water by maintaining high flows of uncontaminated water through 
exposure tanks and by not allowing excess prey to remain in the tanks. Concentrations both in 


108 


the diet and the pore water of the sediments used to contaminate the diet will be documented, 
allowing comparison of water concentrations that directly cause toxic effects with the water 
concentrations needed to result in toxic concentrations in diet. If these experiments demonstrate 
significant toxic effects from diet that might substantially increase risk from water alone, 
additional experiments will be conducted with metal exposure both via water and diet to further 
evaluate the relative importance of these two routes. 

2. Dietary Metals Effects on Zooplankton. 

This will include experiments in which zooplankton (freshwater cladocerans and saltwater 
copepods) are dironically exposed to metals dissolved in water and incorporated into food, both 
separately and combined. Methods will build on those of Hook and Fisher (2001) and Kim et al. 
(2000) and will address the reasons for the discrepancies between these studies and the relative 
importance of these different routes of exposure under conditions expected in natural systems. 
Experiments will be designed to supplement and complement related research in progress 
elsewhere. 

Products 

FY03 Journal article regarding importance of dietary exposure to chronic metal toxicity to 
juvenile fish. 

FY04 Journal article regarding importance of dietary exposure to chronic metal toxicity to 
cladocerans. 

APM 4D FY06 Report evaluating importance of dietary route of exposures to aquatic risk 
assessments for metals (MED). 

Benefits of Products 

The existing controversy over dietary exposure to metals is influencing regulatory decision¬ 
making in several Regions and programs. In addition to the Superfund process and Natural 
Resources Damage Assessment (NRDA) litigation on the CFR, similar issues have been raised 
regarding the assessment of mining impacts on the Coeur d’Alene River in Idaho. With respect 
to State WQS, ORD staff have been contacted by State representatives concerned that shifting 
standards from total to dissolved will increase metal loadings and thereby increase risk from 
metal toxicity via other pathways such as dietary and sediment exposure. In the consultation of 
Region 9 and OW by the Department of Interior on the California Toxics Rule (CTR), the issue 
of dietary exposure to metals figured prominently in the draft Biological Opinion (BO), with DOI 
citing uncertainty regarding dietary uptake as reason for using total, rather than dissolved, metals 
as a measure of compliance. Enhancing understanding of combined waterborne and dietary 
exposures to metal mixtures will improve the ability of environmental managers in the EPA and 
elsewhere to make informed decisions on the assessment of ecological risk from metals in 
aquatic systems. 


109 




Project Title BL Framework for Development and Application of Population Risk~Based 
Criteria for Fish and Wildlife Exposed to Persistent Bioaccumulative Toxicants 

Project Coordination and Resources (3.0 FTEs: AED-1.5, MED-1.5) 

Objectives 

The primary goal of this project is to describe and demonstrate a framework for assessing 
ecological risks and developing risk-based WQC criteria for fish and wildlife populations 
exposed to PBTs, as required by APGl. Currently, national WQC or contaminated sediment 
screening levels are not available for protection of fish and wildlife exposed to PBTs. However, 
ecological risk assessment methodologies, with PBT doses based on residues in tissues of 
organisms, have been under development and extensively discussed for at least a decade (Cook 
et al. 1992). Recently, scientific experts have favorably reviewed the state of the models and 
methods for risks to fish and wildlife populations associated with early life stage toxicity from 
bioaccumulated PBTs. For example, the residue-based, additive toxicity equivalence approach 
for dioxin-like chemicals that act through an aryl hydrocarbon receptor (AhR) mediated 
mechanism of action was examined in great detail in 1998 and found to be ready for application 
in ecological risk assessments (EPA 2001). The fundamental purpose of this project is to insure 
that appropriate chemical residue-based toxicity data and models are effectively used, in 
conjunction with bioaccumulation and population dynamics models, to determine site-specific 
water quality conditions required to sustain populations of aquatic organisms and aquatic- 
dependent wildlife. A general risk assessment framework and associated methods will be 
developed for all PBTs based on the principles contained in EPA’s Guidelines for Ecological 
Risk Assessment (EPA 1998). Also, the PBT framework and associated methods will allow 
probabilistic and spatially explicit representations of population level risks to the extent possible 
and beneficial. 

Scientific Approach 

This project will function as a framework for a continuing development and application of risk- 
based water quality criteria for PBTs. The framework will be based on the conceptual model for 
bioaccumulative toxicants (Figure 11). Three major groups of PBTs that must be addressed are 
halogenated organics, of which the chlorinated organics are preponderant (Carey et al. 1998); 
PAHs which are ubiquitous contaminants of aquatic ecosystems with potential for increased 
aquatic life exposures in the future associated with increasing rates of fossil fuel production and 
combustion (Neff 1979); and organometallic compounds such as methyl mercury which are a 
particular concern for wildlife connected to aquatic food webs (EPA 1997). 

Initially, the conceptual model for risk assessments and criteria development involving 
determination of safe loadings of PBTs to aquatic systems (Figure 11) will be applied to existing 
data and models for chlorinated aromatic chemicals that act through an AhR mediated toxicity 
mechanism in vertebrates. This class of chemicals includes polychlorinated biphenyls (PCBsX 
polychlorinated dibenzo dioxins (PCDDs), and polychlorinated dibenzo furans (PCDFs) for 
which residue-based ELS toxicity data and a mixture toxicity model are available. Thus, 
ecological risk assessment tools and approaches will be described and demonstrated for risk 


110 


based criteria development and application, including use in TMDLs designed to protect 
populations of sensitive fish and wildlife species. The association of the historical lake trout 
population decline in Lake Ontario with exposure of embryos to AhR agonists such as 2,3,7,8- 
TCDD (Cook et al. 1997) will be used as a device to examine the applicability of population 
models when used in tandem with residue based toxicity data. The extent to which extrapolation 
of lake trout risks to other species involves more than species sensitivity to TCDD and degree of 
exposure/bioaccumulation will be examined in the context of toxicokinetic, toxicodynamic, 
biochemical, and life history factors. 

Data gaps and modeling limitations will be identified and further described as research needs for 
development of a general risk assessment capability for all PBTs. This analysis will include an 
initial conceptual evaluation of the degree to which similarities and differences in PBT properties 
and mechanisms of action will define a balance between generic and diemical ^cific PBT risk 
assessment approaches and models. For example, do the properties of persistence and 
bioaccumulation, in combination with principles of population dynamics, indicate that effects on 
ELS development and survival are invariably risk detemiining for PBTs in general? If so, what 
are the basic PBT toxicity and exposure data and modeling requirements for ecological risk 
assessments? Population matrix modeling will be a primary tool for evaluating vulnerability 
differences between species based on differences in life stage sensitivities, exposure profiles, and 
reproductive strategies. 

Other aquatic stressors research projects involving PBTs are expected to contribute periodically 
new or improved risk assessment capabilities which can then be integrated into the PBT 
framework under this project. Although the continuing development of the framework will 
include incorporation of all relevant new data and models, many new capabilities are expected to 
become available as the result of completions of APMs. Examples of expected risk assessment 
capability advances, listed in association with the APGs and projects, are: 

Methods for developing WQC based on characterization of population-level risks of toxic 
chemicals to aquatic life and aquatic-dependent wildlife (APG 3): 

• Rates of metabolism and improved food chain bioaccumulation models for metabolizable 
PBTs (project B2). 

• Models for assessing relative risks of multiple stressors to avian populations with large 
geographic ranges (project B3). 

• Improved understanding of fish early life stage dosimetry including appearance and 
extent of metabolism during development (project B2). 

Models for extrapolating chemical toxicity data across exposure conditions, endpoints, life 
stages, and species (APG 4): 

• Methods for inter-site extrapolation of BAFs based on site-specific food chains and 
bioavailability conditions (project B2). 


Ill 





• PB-TK model/s for inter-species extrapolation of avian exposure and tissue dosimetry 
data consistent with PB-TD models (project B3). 

• Models for bioaccumulation and metabolism of PAHs by fish during ELSs (project B4). 

Approaches for evaluating, at different spatial scales, the cumulative risks from toxic chemicals 
on populations of aquatic life and aquatic-dependent wildlife relative to risks from nonchemical 
stressors (APG 5): 

• Methods for assessing spatial and temporal distributions of risks (project B3). 

• Population models that predict the relative risks of multiple stressors, including toxics 
and habitat alteration, to piscivorous birds (project B3). 

• Methods for assessing PAH risks to feral fish populations with emphasis on vulnerability 
of fish during early life stages to photo-induced PAH toxicity (project B4). 

While integrating new capabilities into the PBT framework, this project will include expansion 
and generalb^tion of the models and methods across chemicals, phyla, and effects to the extent 
possible and scientifically defensible in order to provide site-specific applicability with minimum 
data sets. Scopes of products from this project are therefore subject to the degree of success in 
research fix)m the other PBT projects. Much of this work will be pursued in collaboration with 
OW efforts to update WQC guidelines, and the specific nature and time line of products will be 
developed as part of this collaboration. 

Products 

FY02 Journal article describing a conceptual model for relating risk-based critical residue values 
in fish and wildlife to chemical concentrations in sediment and water (with context of site- 
specific risk-based WQC and assessments of ecological risks associated with contaminated 
s^iments) (Part of APM lA [GPRA # 167]). 

APM lA (GPRA # 167) FY02 Report on integrated water and sediment quality criteria methods 
for assessing site-specific risks of persistent bioaccumulative toxicants to aquatic species (MED). 

Benefits of Products 

This framework will provide guidance for development and application of WQC for protection 
of fish and wildlife populations on the basis of PBT residue-based toxicity data. The framework 
will allow presently available chemical mass balance models to link site-specific chemical 
loading, fate, and transport information to toxicity risks, including population level impacts. 

This, and the demonstration of capability for risk assessment of complex mixtures of PBTs 
having a common mechanism of toxicity, should allow EPA, the States, and Tribes to more 
effectively determine where and to what extent loadings of PBTs to aquatic ecosystems pose 
unacceptable ecological risks. 


112 


(Note: Because of resource reductions after the initial development of this plan, 
the following project will not be pursued at this time.) 

Project Title B2, Incorporate Chemical Metabolism Rates and Site-specific BioavailabUity 
into Bioaccumulation Models Structured for Practical Assessments of Risks to Fish and 
Wildlife Exposed to PBTs 

Project Coordination and Resources (4.0 FTEs: MED-4.0) 

Objectives 

Ecological risk-based criteria for PBTs require a strong capability to relate chemical residue 
based dose-toxicity response data to environmental exposure conditions, and thus to chemical 
loading limitations for protection of vulnerable populations of fish and wildlife. The present 
capability is limited by the amount and quality of data available; significant data gaps (e.g., rates 
of metabolism, bioaccumulation in ELSs); inconsistencies in approaches used for predicting 
bioaccumulation; and uncertainties associated with extrapolation of bioaccumulation data across 
species, life stages, and exposure conditions. This research begins with an objective to develop 
the first data base designed to provide a comprehensive set of BAFs and BSAFs for organisms in 
a complex food web, coupled with bioavailability and metabolism information, so that methods 
for extrapolation of measured BAFs and BSAFs to different ecosystems may be developed and 
validated. The ultimate objective is to extend these models to allow directly application to ELSs 
of fish and wildlife. ELSs are often most sensitive to PBTs, have greatest impact on population 
maintenance, and therefore are risk determining. Specific objectives, associated with 
development and application of the high quality bioaccumulation data base, are: 

• Provide a comprehensive conceptual model for relating risk-based critical residue values 
in fish and wildlife to chemical concentrations in sediment and water. 

• Develop a high quality bioaccumulation data base for a four trophic level, mixed 
benthic/pelagic food web in southern Lake Michigan. 

• Provide a master set of BAFs, BSAFs, and associated bioavailability data with an 
evaluation of procedures for extrapolation to diverse ecosystems and chemical loading 
conditions. 

• Develop guidelines for sampling and analysis of biota, lipid, sediment, water, and organic 
carbon to maximize inter-ecosystem extrapolations and comparisons of bioaccumulation 
data. 

• Determine whole organism based rates of metabolism from the high quality 
bioaccumulation data base for PCBs, PCDDs, PCDFs, PAHs, and other PBTs (as data are 
available). 


113 




• Refine BAFs and models to incorporate metabolism in the food chain and to reduce 
uncertainties associated with species, life stages, and effects end points associated with 
ecological risks for PBTs. 

• Provide a bioaccumulation model for complex mixture of PAHs in fish ELSs suitable for 
assessment of risks to feral fish populations to photo-activated toxicity under project B4. 

Scientific Approach 

Bioaccumulation factors, BSAFs, and food chain models incorporate both generic (non-site 
specific) and site specific elements of bioaccumulation science. A conceptual model for 
integrating high quality bioaccumulation monitoring data with mechanistically based food chain 
bioaccumulation models (e.g., Gobas 1993) will be used for advancing and organizing methods 
for linking chemical residue-based effects data to chemical concentrations in water and 
sediments; and then for risk based WQC development, sediment remediation evaluations, and 
aquatic ecological risk assessments in general. These methods are expected to be an evolutionary 
advancement of bioaccumulation methods presently incorporated into EPA’s Great Lakes water 
quality guidance (EPA 1995b), the methodology for deriving ambient WQC for the protection of 
human health (EPA 2000f), and the framework for application of toxicity equivalence 
methodology for polychlorinated dioxins, furans and biphenyls in ecological risk assessment 
(EPA 2002). The research results will be organized to maximize the accuracy for applying 
general bioaccumulation data and models to site-specific assessments with no or minimum data 
collection on site is required. 

1. Field Data Requirements. 

In general, models and tools for extrapolation and/or prediction can be developed only when 
adequate experimental data are available. One of the major objectives of this research plan is to 
develop field data of the appropriate quality and breadth for im{x*oving bioaccumulation models 
and tools. Breadth of field data includes the completeness of the measurements on all 
components of the food web and its surroundings, and the range of properties associated with the 
chemicals of interest. Furthermore, these field measurements must be intrinsically connected so 
that data from each component are reflective of the conditions sensed or felt by the other 
components. Depending on the ecosystem of interest, measurements over time might be 
required. In this research effort, field data will be developed for all analytes using capillary 
column gas chromatograph/mass spectrometry (GC/MS) techniques with stable isotopes and MS 
resolution of 10,000. The use of analytical techniques with these characteristics will reduce 
uncertainties and biases associated with field data, and when used for all environmental 
components, will provide data of comparable quality among all components of the food web and 
its surroundings. 

Initially, a high quality bioaccumulation data set for a four trophic level, mixed benthic/pelagic 
food web from southern Lake Michigan will be developed for PCBs, PAHs, PCDDs, PCDFs, and 
some chlorinated pesticides. This list of analytes might be expanded after the initial analyses for 
other PBTs which have useful properties for development of bioaccumulation models and tools 
(e.g., chemicals with large molecular weights, > 600 amu, or useful metabolism rates in fish). 


114 


This effort will include samples for components of the food web and its surroundings (i.e., phyto- 
and zoo-plankton, benthic invertebrates, forage fishes, and piscivorous fishes), sediments, and 
water column. In addition, ancillary data such as lipid contents and organic carbon contents will 
be measured. For the purposes of evaluating extrapolation procedures, existing data sets will be 
used and when necessary, high quality measurements will be performed in this investigation. 

2. Determination of Metabolism Rates. 

» 

Mechanistic models for predicting chemical residues in aquatic food webs historically have 
included a rate constant for the metabolic loss of chemical in organisms (Gobas 1993, Thomann 
et al. 1992). Although biotransformation of most all compounds occurs in fishes, common 
modeling practices to date set the metabolic loss rate to zero because many PBTs are thought to 
have rates of metabolism so slow that they can be considered non-metabolizable (although 
metabolism rates have not been measured for nearly all the PBTs). In the absence of measured 
metabolism rates, the default modeling assumption of "no metabolism" is used in modeling 
exercises. This practice of tacitly assuming that metabolism is not important for PBTs has 
largely come about because nearly all the modeling exercises have been perfoimed for PCBs, a 
class of chemicals that bioaccumulate to a degree that suggests extremely low rates of 
metabolism. Consistent with this assumption, the validation efforts to date have shown that the 
food web models have excellent predictive ability for PCBs. 

This research effort will evaluate the use of field data to infer and deduce information about 
metabolic rates for PBTs. The approach for determining rates of metabolism involves the use of 
mechanistic food web models together with high quality field data. With these models and the 
high quality field data, the models can be solved for the rate of metabolism for the chemical of 
interest. In essence, the difference between the model prediction using no metabolism and the 
actual field data is accounted for by the metabolic rate loss parameter if the model parameters are 
set to accurately predict bioaccumulation for non-metabolized congeners. This research effort 
will define for the approach the data quality requirements, and the range of metabolic rates and 
bioaccumulation potential for which the methodology will work. 

3. Bioaccumulation Models for Fish ELSs. 

If metabolism rates can be effectively determined from field data as proposed, the final step in 
bioaccumulation model development for risk assessments involving metabolizable PBTs will be 
the development of empirical and mechanistic bioaccumulation models for fish during ELSs. 

This is a four step process: 1) develop models for bioaccumulation of PBTs (with varying rates 
of metabolism) by female fish, 2) develop maternal transfer models (empirical to PB-TK) to 
predict bioaccumulation by embryos, 3) develop post-spawning bioaccumulation models 
(empirical to mechanistic) to predict uptake and elimination of PBTs (with varying rates of 
metabolism) by fish at different stages of development, and 4) determine inter-species 
differences in ELS bioaccumulation and strategies for inter-species extrapolation of ELS 
dosimetry data. Step 1 is part of the general bioaccumulation model development and step 2 has 
a foundation in field data (e.g., Guiney et al. 1996) and PB-TK models developed (e.g., Nichols 
et al. 1997). Step 3 is an essential element of research on risks of PAHs to fish ELSs under 
project B4. Thus, over time projects B2 and B4 will have an increasing degree of shared research 


115 



objectives. Step 4 requires further consideration of fish ELS dosimetry in the context of the 
mechanism of toxicity associated with the PBT risk being assessed. For example, photo- 
activated toxicity of PAHs in larval fish will involve accumulation of the PAHs in specific 
tissues for which UV light activation is possible. 

4. Inter-Ecosystem Extrapolations. 

The research approach for improving inter-ecosystem extrapolations and comparisons of 
bioaccumulation data will involve the development of appropriate data sets for a variety of 
ecosystem classifications and chemical loading scenarios. With this information, 
bioaccumulation models and tools can be tested and evaluated. The emphasis, initially, will be 
on testing the effectiveness of simpler extrapolation techniques. Extrapolations and comparisons 
of data across ecosystems will involve consideration of differences in the sediment-water 
chemical concentration ratios, departure fi’om steady-state distribution of chemical, food web 
depth (number of trophic levels), and food web composition (benthic-pelagic). The results of 
these comparisons will be used to determine the need for more specific and data intensive 
modeling approaches to reduce uncertainty to a level sufficient for accuracy consistent with 
capability to perform probabilistic risk assessments. 

Products 

FY02 Journal article describing the conceptual model for relating risk-based critical residue 
values in fish and wildlife to chemical concentrations in sediment and water (with context of 
risk-based WQC criteria and contaminated sediment based risk assessments). 

FY02 Technical support document and site-specific methods for determination of BAFs 
associated with the methodology for deriving ambient WQC for the protection of human health 
(MED co-authorship with OW and NCEA). 

FY02 Journal article on BSAFs for PCBs, PCDDs, PCDFs, and PAHs associated with the Lake 
Michigan food web and applicability to other ecosystems. 

FY02 Journal article on validation of the BAF methods used for the methodology for deriving 
ambient WQC for the protection of human health. 

FY03 Journal article on BAFs for PCBs, PCDDs, PCDFs, and PAHs associated with the Lake 
Michigan food web and applicability to other ecosystems. 

FY03 Journal article on proof of concept for measurement of rates of metabolism of PCBs, 
PCDDs, and PCDFs from high quality food web data. 

FY04 Journal article on measurement of rates of metabolism of PAHs from high quality food 
web data with toxicokinetic model interpretation. 

FY04 Report on application of site-specific BAFs and models in conjunction with risk 
assessments requiring extrapolations across species, life stages, and effects endpoints. 


116 


FY05 Journal article or internal report on bioaccumulation of PAHs by fish during early life 
stages of development. 

FY06 Journal article on model/s (including PB-TK) for prediction of bioaccumulation by fish 
during early life stages of development 

Benefits of Products 

The overall benefit is anticipated improvements in bioaccumulation measurements, models, and 
site-specific af^licability, coupled with conceptual clarification of howto incorporate multi- 
media exposure relationships (water, sediment, food chain) into application of chemical residue- 
based toxicity data for development of WQCAVQS, TMDLs, or management of contaminated 
sediments. A specific benefit will be much improved capability, including guidance for sample 
collection and analysis requirements, for extrapolating measured BAFs and BSAFs between 
ecosystems. The capability to provide species and chemical specific rates of metabolism for food 
chain model predictions of bioaccumulation will be a significant improvement since many PBTs 
have reduced bioaccumulation in parts of the food web due to metabolism. Practical and 
beneficial combinations of empirical and mechanistic modeling methods will be defined to 
increase both ease and accuracy of bioaccumulaticHi predictions for site-specific criteria and risk 
assessment applications. Also expected is demonstration of the integration of the basic 
bioaccumulation models with toxicokinetic models as required for PBT criteria and risk 
assessments involving particular modes of action and critical effect end points. 

Project Title B3. Multiple Stressor Risks to Common Loon and Other Piscivorous Bird 
Populations (cross-listed in Section 4, Habitat Alteration, Project 4) 

Project Coordination and Resources (6.9 FTEs: AED-5.4, MED-1.5) 

Objectives 

This project has been developed as a demonstration and evaluation of the utility of the research 
approaches described in NHEERL’s WRS (EPA 2000e). The wildlife strategy describes research 
approaches that address key research needs, including improved capabilities for cross-species 
extrapolation, prediction of population dynamics in spatially-explicit habitats, assessment of the 
relative risk of chemical and non-chemical stressors, and definition of appropriate spatial scales 
for wildlife risk assessments. This project is one of several that will be developed to evaluate the 
approaches and key hypotheses regarding risks to wildlife species that are described in the 
wildlife strategy. In addition to its consistency with these overall objectives, this specific project 
was identified because it involved minimal data collection activities and addressed a problem of 
immediate concern to the Agency. 

The overall objective of this demonstration project is to develop the tools and approaches for 
assessing the risks of multiple stressors to populations of piscivorous wildlife, leading to the 
development of risk-based criteria. Three major research objectives include: 


117 


• Develop approaches for predicting population-level responses to stressors, and identify 
the responses at the individual level that have the greatest influence on population-level 
responses (APG 3). 

• Develop mechanistically-based approaches for extrapolating toxicological data across 
wildlife species, media, and individual-level response endpoints (APG 4). 

• Develop approaches for evaluating the relative risks from chemical and non-chemical 
stressors on spatially structured wildlife populations across large areas or regions (APG 

5). 

The research described here attempts to make advances in each of these research areas through a 
single demonstration project designed to develop the tools and approaches necessaiy to conduct a 
multi-tiered assessment of the risks of PBTs, (e.g., mercury) to populations of piscivorous birds 
in New England and the upper Midwest. In the process of developing the approach and tools for 
conducting the risk assessment, we will also develop a framework for establishing wildlife 
criteria using piscivorous birds and Hg as the example. In conjunction with project Bl, the 
methods and models developed in this demonstration project will be evaluated for their 
applicability to other PBTs and other wildlife species. This demonstration project focuses on 
issues starting with the exposure of birds to mercury in the fish (and other dietaiy components) 
they consume, rather than focusing on fate and bioaccumulation within the wholly aquatic 
portion of the food web. Many of the issues addressed in the development of population and 
spatial models in this project should contribute to the development of more generalized 
approaches for assessing risks to wildlife species. 

It should also be noted that another component of this project involves the assessment of the 
interactive effects of landscape-level habitat alteration and mercury on loons. This project also is 
described in Section 4 (Habitat Alteration) because there are significant research issues regarding 
habitat alterations, including evaluating the spatial configuration of loon habitat and mercury 
impacts in the landscape mosaic and the issue of scaling up from local to regional impact 
assessments. 

Scientific Approach 

Mercury contamination remains a high priority issue for several EPA Program Offices and 
Regions because of widespread atmospheric deposition and concerns of accumulation through 
aquatic food webs. Although there is evidence of reduced productivity in some piscivorous birds 
and widespread reports of wildlife tissue mercury concentrations exceeding levels associated 
with adverse effects in controlled studies, it is unclear what impact this has on the viability of 
populations of piscivorous wildlife. Also, mercury contamination exists within a patchwork of 
other co-occurring stressors to wildlife populations, but the relative risks are poorly understood. 
Because mercury bioaccumulates in the aquatic food web, this demonstration project focuses on 
the risks of mercury to top level predators (piscivorous birds, in particular, common loons [Gavia 
immer]), associated with mercury exposure in the environment. Given the heterogeneous 
distribution of stressors (e.g., dietary methyl mercury, habitat degradation, acidification, human 
disturbance), the project will attempt to identify the spatial relationships among stressors (i.e.. 


118 


correlations in distributions), the potential interactions among stressor impacts, and the relative 
risks among potential stressors to populations of loons at various spatial scales. The research 
will secondarily characterize risks for the belted kingfisher {Ceryle alcyon\ another piscivorous 
bird that is often the focus of ecological risk assessments because its small body size and high 
food ingestion rate lead to estimates of high potential risk. 

To improve the process of developing risk-based criteria for mercury protective of loons and 
other piscivorous birds, advancements in model and method development and data acquisition 
are needed in five major areas. First, the landscape of interest needs to be characterized, 
including the spatial and temporal distribution of stressors and available habitat for species of 
interest Second, stressor-response relationships are needed, especially for endpoints related to 
survival and fecundity rates. Relationships may be developed empirically fi’om field data or 
generated in laboratory tests based on representative exposure scenarios. Third, methods for 
interspecies extrapolations of stressor-response relationships for mercury are needed. Fourth, 
age-class matrix population models incorporating stressor-response relationships are needed for 
loons and other piscivorous birds. Fifth, population dynamics need to be assessed across 
heterogeneous landscapes where variable stressor levels and habitat qualities influence the 
distribution of populations. Information from the five areas is connected through important 
feedback loops (e.g., knowledge of population dynamics captured in the matrix models also 
informs the selection of landscapes and endpoints for dose-response testing). Research on 
landscape characterization, population model development, and determining and extrapolating 
dose-response information will proceed in parallel. The level of refinanent needed in the models 
and acquired data is a function of the degree of uncertainty acceptable for setting criteria, so the 
application of tools will explicitly consider the availability and quality of data for various tiers of 
risk assessment. 

1. Landscape Characterization. 

A demonstration project on risks to loons would focus on landscapes in the upper Midwest and 
Northeastern United States. Characterization of these landscapes would include: 

• Collection of mercury residue distribution in fish and water bodies across the landscape. 
This would be based primarily on a synthesis of existing monitoring data from Federal, 
State, academic, and non-profit organization sources. 

• CharacterizaticMi of habitat quality for species of interest across the landscape. For lotxis, 
key habitat characteristics would include presence of suitable nesting and brood rearing 
sites, measures of human disturbance, density or extent of human dwellings and other 
activities around lakes, turbidity, and the availability of suitable forage fish supplies. 

This information would be synthesized from available monitoring databases and/or aerial 
photographs. 

• Collection of data on the abundance and distribution of loons and other piscivorous birds, 
and on juvenile production rates by location. This information would be based on long¬ 
term loon monitoring programs that exist in the upper Midwest and the Northeast. 


119 


2. Stressor-response Relationships. 


Although information currently exists on the toxicity of mercury to several bird species, none of 
the tests were conducted in such a way that dose-response relationships can be estimated and 
none were conducted with carnivorous species. In fact, testing of avian reproduction effects is 
rarely done with the intent of defining dose-response relationships for population-related 
endpoints. Development of dose-response relationships would include: 

• A controlled dosing study to determine dose-response relationships for methyl mercury 
effects on survival and fecundity of American kestrels {Falco sparverius). Husbandry 
methods for breeding piscivorous birds in captivity generally have not been developed, 
but the kestrel has a long history of successful breeding under laboratory test conditions. 
This study is currently being conducted in cooperation with USGS Patuxent Wildlife 
Research Center and NCEA. 

• Empirical development, from existing field monitoring data, of stressor-response 
relationships for measures of productivity for other types of stressors, including habitat 
impairment, human disturbance, and lake acidification. It will be more difficult to show a 
relationship of these stressors to adult survival. 

3. Interspecies Extrapolation. 

Extrapolation methods are needed for estimating toxicity in untested species from tested species, 
from laboratory tests to free-flying wildlife, and across media. Extrapolation of toxicity 
information includes: 

• Development of PBTK/TD models for methyl mercuiy in kestrels for predicting effects in 
loons and other piscivorous birds. Mercury residue information from studies with 
kestrels will be used to develop residue-response relationships sensitive to the duration of 
exposure. The PBTK/TD model then will be used to predict the movement and effects of 
methyl Hg in other species. This model will be developed through a cooperative project 
with USGS (Patuxent) and NCEA. 

• Empirical extrapolation methods based on a synthesis of existing toxicity databases. 

These methods estimate a distribution of sensitivity across tested species, but are poor 
predictors of where a specific species would fall in that distribution. This work would 
build on existing syntheses projects by including new data from the kestrel studies and 
other published data. 

4. Population Matrix Models. 

Age-class matrix models will be used to organize information on the population dynamics of 
loons and other piscivorous birds. Development of matrix models would include: 

• Integration of life history information including adult and juvenile survival, fecundity, 
immigration and emigration rates, and density dependent factors into the model 


120 


framework. This information largely will be synthesized from existing monitoring 
databases and literature. A data system for population modeling parameters will be 
evaluated with NCEA potentially as a component of the Wildlife Exposure Handbook 
and the NCEA-supported Wildlife Canada Exposure Model. Current monitoring efforts 
could also be focused to include measurement of this information. 

• Development of approaches for integrating individual-level responses in order to 
extrapolate and predict population-level effects of anthropogenic stressors. This model 
development would be a primary objective of this demonstration project. 

• Identification of those responses at the individual-level that have the greatest influence on 
population-level responses (i.e., elasticity analysis). This analysis would be part of this 
demonstration project. 

Products 

FY03 PBTK/TD model for predicting individual effects of chronic mercury exposure to facilitate 
cross species extrapolation of toxicity responses. 

APM 3 A (GPRA #59) FY04 Population models that project the relative risks of multiple 
stressors (toxic chemicals, habitat alterations) to piscivorous birds (AED, MED). 

APM 5B F Y06 Approaches for addressing spatial scale issues in assessing risks of multiple 
stressors to wildlife populations in spatially-diverse landscapes (AED, MED). 

Also see Section 4 Habitat Alteration, Project 4 for associated products. 

Benefits of Products 

This demonstration project will directly address APG 3 by providing methods for developing 
WQC based on characterization of population-level risks of toxic chemicals to aquatic-dependent 
wildlife. 

Given the paucity of comparative toxicity data across taxonomic groups of wildlife, the 
development of a PBTK/TD model for mercury in birds will improve the capability for 
extrapolating chemical toxicity data across endpoints, life stages, and species of wildlife (APG 
4). 

The focus of this demonstration project, understanding stressor risks of individuals in the context 
of effects at the level of populations in spatial ly-di verse landscapes, will provide approaches for 
evaluating the relative and cumulative risks from toxic chemicals and non-chemical stressors on 
populations of aquatic-dependent wildlife (APG 5). 

Project Title B4. Risks to Fish Populations from PAHs in Natural Systems 
Project Coordination and Resources (4.0 FTEs: MED-4.0) 


121 


Objectives 


A major uncertainty in assessing the risks of PBTs to aquatic life is whether application of 
laboratory toxicological data adequately reflects complex exposure relationships and interactions 
important to responses in natural systems. Chemicals in one class of PBTs, PAHs, have been 
found in traditional laboratory tests to have relatively low toxicities, due both to a nonspecific 
mechanism of action and to reduced bioaccumulation in some organisms because of metabolic 
transformations of these chemicals. However, the toxicities of some PAHs to various aquatic 
organisms have been demonstrated to be greatly increased (by orders of magnitude) due to 
exposure to UV radiation (Bowling et al. 1983; Cody et al. 1984; Kagan et al. 1984, 1985; Oris 
and Giesy 1985, 1987; Newsted and Giesy 1987; Holst and Giesy 1989; Tilghman Hall and Oris 
1991; Huang et al. 1993; Buckler et al. 1994; Ankley et al. 1994, 1995, 1997; Boese et al. 1997; 
Erickson et al. 1999). General principles of dosimetry for this enhanced toxicity, based on PAH 
accumulation and UV intensity, have been described (Newsted and Giesy 1987; Ankley et al. 
1995, 1997; Erickson et al. 1999). 

An analysis of fuel contamination of the clear waters of Lake Tahoe concluded that 
photoactivated toxicity posed a significant risk to zooplankton (Oris et al. 1998) and current data 
suggest that ELS fish in PAH-contaminated littoral zones of the Great Lakes are at risk (Mount et 
al. 2001). However, this risk is uncertain due to several factors, some specifically related to 
photo-activated toxicity and some of more concern to PBTs in general: 1) most research to date 
has used laboratory UV light sources with spectra different from natural sunlight; 2) both the 
intensity and spectra of UV light in natural systems vary spatially and tempOTally, resulting in 
receptor organisms receiving widely varying exposures depending on their life habits and the 
properties of the system; 3) PAH exposure can also vary widely within natural systems, 
especially between sediments and overlying waters, so that PAH accumulation can also depend 
on organism attributes and system properties; 4) the accumulation, and thus the effects, of PAHs 
can vary between laboratory and natural systems due to food chain influences and maternal 
transfer to young organisms; 5) the relative accumulation of and sensitivity to PAHs of different 
life stages are poorly known; and 6) research has usually used individual compounds or simple 
mixtures of commercially-obtained PAHs, in contrast to complex mixtures of PAHs occurring in 
contaminated natural systems. However, current knowledge of a) the general levels of PAH 
contamination and the magnitude of UV light in natural systems and b) the sensitivity of many 
organisms to photo-activated toxicity in the laboratory indicate a potential for major impacts due 
to these interacting factors. 

Risks from PAHs will depend on a complex interaction among light, chemical, receptor 
organisms, and system characteristics. ELS fish are one group of organisms at potential risk. 

ELS fish potentially can have significant PAH accumulation due to maternal transfer to eggs, 
exposure of eggs to water and sediment, accumulation after hatching from food and water 
(especially when closely linked to sediments), and the absence of metabolic pathways which 
limit PAH accumulation in older fish. Provided that the fish behavior or the system attributes 
result in significant exposure to light, ELS fish might be particularly susceptible to UV-activated 
PAH toxicity because of their small size (i.e., large surface to volume ratios and short penetration 
distances) and lack of protective pigmentation and gill coverings. Past research has shown early 
life stage fish to be susceptible to photo-activated toxicity (Bowling et al. 1983; Oris and Giesy 


122 


1985, 1987; Tilghman Hall and Oris 1991; Buckler et al. 1994), but use of this research to assess 
risk in natural systems is limited by the factors discussed above. 

The overall objective of this research project is to develop more comprehensive and accurate 
assessments of the risks of PAHs to early-life-stages of fish that address the influence of UV 
radiation and exposure relationships in natural systems. To this end, PAH accumulation in ELS 
fish will be evaluated both in the laboratory and in natural systems; effects of accumulated PAH 
in ELS fish will be evaluated both under laboratory UV light and natural sunlight for both 
individual PAHs and PAH mixtures from contaminated systems; and likely risks based on these 
observed effects and on fish habits will be estimated for natural systems. This project will not 
address the incor|X)ration of effects on ELS fish into fish population models, but will provide 
information important to population-level assessment methods such as those developed in 
projects B1 and B3 and also will examine the correlation of expected ELS effects with fish 
community health indices. Extensions of this work to general PBT assessments will be 
addressed in project B1. 

Scientific Approach 

Assessing the risks of PAHs to ELS fish requires consideration of several factors. First, 
environmental PAH concentrations must be characterized, including chemical partitioning 
information important to bioavailability. Second, UV radiation exposures must be evaluated 
relative to fish behavior and environmental conditions. Third, the accumulation of PAHs must 
be estimated as a function of fish age and environmental exposure concentrations, including 
consideration of maternal transfer, uptake by both egg and fty, multiple routes of exposure, and 
age-dependent metabolism. Fourth, good dosimetry relationships are needed which link 
mortality and growth to PAH accumulation in the fish, to the varying levels of UV radiation they 
receive, and to their age. Fifth, the combined effects of complex mbctures of PAH must be 
evaluated, including the effects of those PAHs which are not measured. This project will 
develop needed capabilities in these areas and will assess the likely risk of PAHs to ELS fish in 
selected natural systems. 

1. ELS Accumulation of PAHs. 

Meaningful assessment of the effects of PAHs (and other PBTs) on ELS fish requires that 
toxicity test results be applied with consideration of the importance of PAH accumulation to 
organism response and how accumulation might differ from that in natural systems. Such 
consideration should include not only accumulation after hatch from water and food, but also 
uptake from water during egg incubation and transfer from the maternal fish to the egg. The 
importance of these different routes of exposure depends on how rapidly PAHs accumulate 
during different life stages and at what life stages UV exposure is most important. Laboratory 
experiments will examine the accumulation of PAHs in ELS fish as a ftmction of when exposure 
starts (parent fish, egg, newly hatched fty) to determine if PAHs originating from parents or 
taken up by eggs might significantly contribute to risks. Uptake relationships will be monitored 
as fish grow to determine at what age and to what extent metabolic transformation of PAHs 
becomes important for regulating accumulation. Accumulation also will be measured fiom ELS 
fish collected from and exposed in natural systems to determine its relationship to environmental 


123 


PAH concentrations and to compare this with laboratory results. Results of these efforts will 
contribute to development of ELS fish bioaccumulation models for PBTs under project B2. 

2. Dosimetry Relationships for PAH Toxicity to ELS Fish. 

Effective assessment of the risk of PAHs to fish populations requires improved understanding of 
the relationship of PAH accumulation, UV intensity and spectmm, and fish age to various effects 
in ELS fish. Dosimetry relationships will be developed in the laboratoiy for selected individual 
PAHs. Experimental methods will be developed to allow continuous exposure of ELS fish to 
various levels of both PAHs and UV radiation. Tests will be conducted to address the effects of 
PAH levels, the duration/nature of prior exposure to PAHs, fish age, and UV intensity, duration, 
and variability. 

A major impediment to PAH risk assessments is the complex mixtures that occur in most PAH- 
contaminated natural systems. Even if such mixtures could be completely characterized, the 
potencies of most constituents would be unknown and the combined toxicity could be 
significantly underestimated. The unaccounted-for toxicity could be addressed with an index 
which compares total measured photo-activated toxicity from a complex mixture to the expected 
toxicity based on measured accumulations of a subset of mixture constituents for which photo- 
activated potencies are known. Such an index could be based on the oligochaete Lumbriculus 
variegatus, for which phototoxicity bioassays are relatively simple and precise, and can involve 
either water or sediment exposures. The phototoxicity of complex PAH mixtures from selected 
sites and of reference PAHs to both L. variegatus and ELS fish will be tested to determine the 
utility of such an index. 

3. Assessment of PAH effects in Natural Systems and the Use of Photo-activated Potency as an 
Ecological Indicator. 

The effects of PAHs on ELS fish in selected natural systems will be evaluated using floating 
experimental platforms containing chambers in which fish can be exposed to site water and to 
various levels of UV radiation through selective wavelength screening. Such systems will 
provide a direct test of the legitimacy of concerns about photo-activated toxicity for natural levels 
of PAHs and UV radiation, and provide data for testing the applicability of laboratory data and 
methods to the prediction of these effects. 

MED scientists will also work cooperatively with researchers from the Great Lakes Ecological 
Indicators (GLEI) project to evaluate photo-activated PAH toxicity potency in sediments as an 
ecological indicator. GLEI staff will collect sediments samples from a stratified random set of 
coastal wetlands and tributaries to the Great Lakes. Samples will be shipped to MED and 
evaluated for photo-activated PAH toxicity potency using the oligochaete L. variegatus as 
described above. The measured potency of PAHs in these sediments will be compared to 
chemical characterization being conducted by the University of Minnesota cooperators, and 
indices of UV transmission in the water column, to estimate relative risk from this pathway at 
each site, using the photo-activated PAH toxicity models developed by MED in this and previous 
research. Additionally, estimated risk from photo-activated toxicants with be examined relative 


124 


to measures of biological condition being collected for each site by GLEI staff to evaluate 
measured photo-activated PAH potency as an ecological indicator. 

Products 

FY03 Journal article on bioaccumulation of PAHs in ELS fish. 

FY04 Journal article on dosimetry of photoactivated PAH toxicity to ELS fish. 

FY05 Journal article on effects of complex PAH mixtures and ambient sunlight to ELS fish in 
natural systems. 

APM 5 A FY05 Report regarding assessment of risks to aquatic organisms from combined 
exposure to PAH mixtures and UV light in natural systems (MED). 

Benefits of Products 

Research to date has indicated that current PAH risk assessments do not adequately handle such 
issues as photo-activated toxicity, incompletely-characterized complex mixtures, and ELS 
exposure issues, so that risk might be greatly underestimated. However, there has been no direct 
evidence that risk is significant for the combinations of PAH contamination, UV light, and 
biological receptors in natural systems. This work will determine whether there should be 
concern regarding such toxicity for fish populations in PAH-contaminated areas. If this is the 
case, this would greatly impact the development of WQC and sediment guidelines for PAHs, as 
well as hazard assessments for other classes of PBTs which might require similar considerations. 
This work will also help improve general PBT exposure assessment methodolo^es, such as fish 
ELS accumulation models. 

Gap Analysis 

The overall goal of this research program is to develop procedures for risk-based criteria for toxic 
chemicals. The Critical Path subsection described the research needed to better describe the risks 
of toxic chemicals to aquatic life and aquatic-dependent wildlife populations and communities. 
The proposed research projects address these needs. Although the research program will expand 
and improve the approaches for developing criteria, this gap analysis reviews the needs (e.g., 
models, test meth^s, toxicity data, methods for assessing temporal and spatial distributions of 
exposure, and role of life history on species vulnerability) that will not be completely addressed 
by this program, but are required for development of a complete risk assessment capability for 
toxic chemicals. 

Developing the scientific basis for risk-based criteria will require the overall conceptual 
framework and better modeling tools described in the research projects outlined above. While 
the focus of this research program is on the development of methods and models, there will be 
parameters required by these methods and models that can not be met adequately with existing 
data sources alone. New data will be generated for specific questions in a couple of our 
demonstration projects; however, a major gap for achieving our overall goal of developing risk- 


125 


based criteria for chemicals will be the generation of new data for several steps on the critical 
path. For example, risk-based criteria will require data on dose-response relationships for 
various chemicals exposed to aquatic life and wildlife, but very few existing chronic exposure 
toxicity tests generate dose-response relationships, and we are not proposing new testing. Also, 
we are developing the basis for PBTK modeling that will be applied to a demonstration project 
for extrapolating toxicity estimates to other species, but more will be needed for generating 
physiological and metabolic information for broader applications of PBTK models. Finally, our 
demonstrations projects will use existing life history information, such as survival and fecundity 
rates, for setting parameters in population models, but scientifically-defensible population 
models for most aquatic and wildlife species will require new life history data that will not be the 
focus of this research program. These needs for new data for various models will be discussed 
further below. 

Specific gaps are related to key components of the conceptual models for nonbioaccumulative 
and bioaccumulative toxicants (Figures 10 and 11). They are organized under the following 
headings: bioavailability, dosimetry and bioaccumulation, toxicity, and population models. 

Bioavailability 

One gap relates to the need to better understand exposure, which is outside of NHEERL 's 
mission: 

1. Exposure models are needed that can estimate physical transport and fate of chemicals. This 
includes persistence/degradation, partitioning, and especially chemical forms reaching aquatic 
organisms. Improved models will be needed for predicting chemical forms of metals and 
activities of organic chemicals in water, bulk sediments, and associated pore waters. 

Two gaps relate to the needs for extending our modeling approaches to other chemicals and 
exposure scenarios: 

2. Although this plan will, initially, develop models for ammonia and metals, gaps will remain 
for applying these models to other nonbioaccumulating chemicals. Organic chemicals with low 
bioaccumulation potential are an important class of chemicals which pose ecological risks 
through a variety of mechanisms of toxicity including disruption of endocrine functions. Present 
and future research in and outside of NHEERL on the effects of these chemicals requires models 
and data for exposure and bioavailability appropriate for linking the toxicology research results 
into criteria development and ecological risk assessment procedures. 

3. Seasonal and spatial variability in bioavailabilities of chemicals, coupled with life stage 
changes, population movements, and mechanisms for avoidance of exposure, produce 
complications for determination of species vulnerabilities. This gap is presently being addressed 
only in the proposed research for loons (project B3). 


126 


Dosimetry and Bioaccumulation 

Two gaps relate to the development of PBTK models for improving understanding of dose- 
residue-response relationships and extrapolation among species or life stages: 

1. Although the mercury-loon project (B3) will develop a PBTK/TD model in kestrels for use in 
estimating the toxicity of mercury to piscivorous birds, the toxicokinetics of mercury are not 
representative of other PBTs. This will limit the general q^plicability of these models to other 
chemicals. 

2. Another gap for developing avian PBTK models is the paucity of information on avian 
physiology and metabolism for setting parameters in the models. 

Several gaps relate to improvements needed in bioaccumulation information: 

3. A method for determination of rates of metabolism for PBTs is needed in order to allow 
accurate predictions of bioaccumulation with aquatic food chain models. If the research 
proposed to demonstrate the feasibility of determining rates of metabolism from field data fills 
this important risk assessment need, additional studies will be required to provide the metabolism 
data for all PBTs of concern in divCTse food webs. 

4. Existing BAFs, BSAFs, and food chain models are based on whole adult organisms and thus 
may not be sufficient when dose to ELSs and/or specific tissues must be evaluated. ELS 
dosimetry-based BAFs and PB-TK models are needed to fill this gap. The metabolism rate gap 
extends to bioaccumulation of PBTs like the PAHs in embryo-larval stages of fish with potential 
vulnerability to photo-induced toxicity. Proposed photo-induced PAH toxicity research provides 
a beginning for filling this large gap. Additional research will be required to establish a general 
ELS toxicity risk assessment capability equal to that available for juvenile or adult organisms 
exposed to a wide variety of chemicals. 

5. Very few bioaccumulation data sets are of sufficient quality to validate the uses of BAFs and 
BSAFs, especially when extrapolated across species and/or ecosystems. The intent of proposed 
NHEERL research is to maximize the capability for extrapolation of BAFs and BSAFs for PBTs. 
However, consistent data gathering efforts sponsored by Offices interested in the application of 
BAFs and BSAFs for criteria development and risk assessment are needed in order to provide 
measures of uncertainty involved in such extrapolations as well as bioaccumulation model 
predictions performed without calibration (site-specific measurement of BAFs and BSAFs). 

6. Comprehensive and toxicity hazard assessment compatible BAFs, BSAFs, and food chain 
models are needed to meet the requirements of joint action toxicity models such as for TCDD 
toxicity equivalence or photo-induced PAH toxicity. 


127 


Toxicity 


Two gaps relate to the needs for toxicity data: 

1. Critical to the idea of "risk-based" criteria is the need to understand the relationship between 
stressor intensity and responses affecting survival and reproduction (i.e., dose-response 
relationships). Although acute toxicity tests for aquatic life and wildlife usually produce dose- 
response relationships, most chronic exposure test and reproduction tests are designed to estimate 
effects thresholds (e.g., no observed adverse effect levels [NOAELs]) rather than describe dose- 
response relationships. Although some fish reproduction tests have been conducted with 
sufficient number and spacing of concentrations that dose-response relationships could be 
estimated, almost no avian reproduction testing has been done to quantify dose-response 
relationships. This is a large data gap, and OW presently may have no mechanism for generation 
of new toxicity data. 

2. Because of their hydrophobicity, a majority of PBTs accumulate in the benthos of freshwater 
and marine systems. From benthic environments, PBTs transfer to higher tropic levels where 
adverse effects to wildlife may occur. Despite the acknowledged effects of PBTs at higher tropic 
levels, a gap exists in our knowledge of whether or not this class of chemicals also causes 
significantly adverse effects to benthic organisms, populations, and communities. 
Bioaccumulation data for benthic organisms from contaminated sites around the country indicate 
exposure is occurring but we are not certain of the effects. To insure benthic ecosystems and 
resources are fully protected this gap should be addressed. 

Other gaps relate to the extrapolability of toxicity data among species and endpoints: 

3. Compared to aquatic organisms, the database of chronic exposure tests of effects on avian and 
mammalian survival and reproduction is much more limited, with much of the testing of non¬ 
pesticide chemicals done without standardized procedures. Consequently, the database for 
making interspecies extrapolations for wildlife is insufficient to significantly improve methods 
based on comparative toxicity relationships among species. This research program will be 
adding little new data for improving species sensitivity relationships for wildlife. Also, previous 
analyses of avian toxicity databases demonstrated no relationship between acute and chronic 
toxicity measurements for birds. 

4. Virtually unexplored are the TKTD determinants for interspecies and inter-elfect 
extrapolations of potency ratios required for PBT mixture toxicity risk assessment using a toxic 
units model approach, such as the additive TCDD toxicity equivalence model. 

Several gaps relate to the need for improved toxicity models and databases: 

5. Although models for predicting effects from fluctuating exposures models are proposed for 
development for metals and ammonia, a gap exists in this capability for organic chemicals. 

6. Residue-based toxicity data bases need to be advanced and evaluated for applicability to 
aquatic ecological risk assessment requirements for PBTs. 


128 


7. Complex, multi-stressor models, such as required for photo-induced PAH toxicity to fish 
during embryo-larval stage of development, need to be developed and applied to determine the 
magnitude of ecological risks which are presently highly uncertain. 

Other important gaps: 

8. In some cases, populations of chronically exposed aquatic organisms have demonstrated an 
evolved tolerance or genetic resistance to toxicity through chronic exposure. The potentially 
enormous and irreversible consequences of rapid evolutionary change suggest the importance of 
better understanding, predicting, and managing these anthropogenic impacts on aquatic and 
wildlife populations. In addition, technological advances now permit an identification of the 
genetic changes that may provide the key to understanding the mechanisms by which populations 
and species adapt or become extinct. 

9. A gap exists in the development of biological indicators that can lead to diagnosis of 
developing toxicity problems in aquatic ecosystems before population impacts are observable. 

For example, commonly measured biochemical effects, such as P450 enzyme induction, are 
sometimes used as diagnostic indicators of exposure to specific categories of chemicals. 
However, these measurements are of limited use because their relationships to organismal, much 
less population-level, risks are not well understood. 

10. NHEERL toxicology research plans organized outside of Goal 2 should consider criteria and 
aquatic ecological risk assessment needs in order to prevent gaps for the utilization of the 
research products to meet aquatic stressor data and methods requirements. 

Population Models 

Several gaps relate to the needs for information for developing population models: 

1. The greatest limitation to the application of population matrix models is the paucity of high 
quality data on mortality and fecundity rates, and our limited understanding of density-dependent 
feedbacks and other ways populations compensate for losses due to stressors. Generalized 
population models are helpful for identifying information needs, but scenario-specific population 
models often will be limited by the lack of data about the populations of interest. Specific case 
studies have been chosen because of their relative wealth of population parameter information 
and the potential to identify the types and formats of data required for population models when 
applied to other species/region/stressor(s) combinations. The collection of basic life history 
information on fish and wildlife species is a gap not addressed by this effort outside of a few 
species covered under case studies. 

2. Although life history infoimation is being gathered by State and Federal resource agencies and 
others, it is not always in an adequate form for use in a population model. In addition to 
proposing methods for population modeling, we need to work with resource agencies to 
influence the format of life history information being gathered to improve its utility in risk 
assessment. 


129 


3. Complex mixtures of PBTs are the norm, so interspecies differences in potency, as well as in 
bioaccumulation, for individual chemicals in the mixture must be factored into population level 
risk predictions. 

4. Is absence of overt mortality, even for ELSs, an adequate effects end point for preventing 
population declines caused by PBTs or non-PBTs? If not, how do we determine what is adequate 
for aquatic invertebrates, fish, amphibians, or avian and mammalian wildlife? The presently 
proposed toxic chemicals research will fill this fundamental gap only to the extent that specific 
chemicals are intensively used as foci for development of models and risk assessment methods. 

5. A national WQC methodology for different classes of PBTs needs definition, through use of a 
generic population model (or a suite of generic population models) of species characteristics, life 
stages, and toxicity effects that are most predictive of risks to populations, regardless of site 
conditions. This information will fill a gap which presently limits development of population 
level based, chemical-specific criteria, as well as determination of site-specific model and data 
requirements for application of the criteria. 

Two gaps relate to the need for demonstrating and verifying the usefulness ofpopulation models: 

6. Population models need to be developed and applied through case studies to explicitly 
demonstrate risk assessment requirements for prediction of adverse population impacts as a result 
of PBT toxicity-caused reductions in survival of aquatic organisms. 

7. The proposed approaches in this researdi plan rely heavily on the accuracy of population 
models. The utility of these approaches must be evaluated through field verifications. More 
broadly, it must be better understood how different model types and levels of complexities are 
necessary to achieve the desired reduction in uncertainties for specific assessment needs. Only 
one project in this program addresses this need. 

One gap relates to the importance of spatial scales for aquatic life assessments: 

8. Spatially explicit population models are often required for assessment of risks to aquatic- 
dependent wildlife populations that function in landscapes that integrate many lakes, wetlands, 
and streams. Because PBTs tend to distribute widely, if not uniformly, for long periods of time 
in aquatic habitats, uncertainty exists for when, and the extent to which, spatially explicit 
population models are requisite for assessment of risks to populations of aquatic organisms. 

References 

Aldenberg, T., Slob, W. 1993. Confidence limits for hazardous concentrations based on 
logistically distributed NOEC toxicity data. Ecotoxicol Environ. Saf 25:48-63. 

Ankley, G.T., Collyard, S.A., Monson, P.D., Kosian, P.A. 1994. Influence of ultraviolet light on 
the toxicity of sediments contaminated with polycyclic aromatic hydrocarbons. Environ. Toxicol. 
Chem. 13:1791-1796. 


130 


Ankley, G.T., Erickson, RJ., Phipps, Gi., Mattson, V.R., Kosian, P.A., Sheedy, B.R., Cox, J.S. 
1995. Effects of light intensity on the phototoxicity of fluoranthene to a benthic 
macroinvertebrate. Environ. Sci. Technol 29:2828-2833. 

Ankley, G.T., Erickson, R.J., Sheedy, B.R., Kosian, P.A., Mattson, V.R., Cox, J.S. 1997. 
Evaluation of models for predicting the phototoxic potency of polycyclic aromatic hydrocarbons. 
Aquat. Toxicol. 37:37-50. 

ASTM. 1988. Standard practice for conducting acute toxicity tests with fishes, 
macroinvertebrates, and amphibians. Designation: E 729-88. In 1988 Annual Book of ASTM 
Standards^ Vol. 11.04. American Society for Testing and Materials, Philadelphia, PA, pp. 304- 
322. 

Baker, J.L., Barefoot, A.C., Beasley, L.E., Bums, L., Caulkins, P., Clark, J., Feulner, R.L., Giesy, 
J.P., Graney, R.L., Griggs, R., Jacoby, H., Laskowski, D., Maciorowski, A., Hihaich, E., Nelson, 
H., Parrish, R., Siefert, R.E., Solomon, K.R., van der Schalie, W. 1994. Aquatic Dialogue 
Group: Pesticide Risk Assessment and Mitigation. Society of Environmental Toxicology and 
Chemistry, Pensacola, FL. 

Boese, B.L., Lamberson, J.O., Swartz, R.C., Ozretich, R.J. 1997. Photoinduced toxicity of 
fluoranthene to seven marine benthic cmstaceans.rc/z. Environ. Contam. Toxicol. 32:389-393. 

Bonnet, C., Balbut, M., Ferard, J.-F., Martel, L., Garric, J. 2000. Assessing the potential toxicity 
of resuspended sediment. £«v/ron. Toxicol. Chem. 19:1290-1296. 

Bowling, J.W., Leversee, G.J., Landmm, P.F., Giesy, J.P. 1983. Acute mortality of anthracene- 
contaminated fish exposed to sunlight. Toxicol. 3:79-90. 

Breck, J.E. 1988. Relationships among models for acute toxicity effects: applications to 
fluctuating concentrations. Environ. Toxicol. Chem. 7:775-778. 

Buckler, D.R., Kemble, N.E., Echols, K.R., Mount, D.R., Tillitt, D.E. 1994. Photoactivated 
toxicity of PAH to endangered fishes and standard laboratory test species. Abstracts^ 15* Annual 
Meeting, Society of Environmental Toxicology and Chemistry, Denver, CO, October 30- 
November3, 1994. 

Burgess, R.M., Ryba, S.A., Cantwell, M.G. 2000. Importance of organic carbon quantity on the 
variation of k^,,. in marine sediments. Toxicol Environ. Chem. 77:9-29. 

Calvo, C., Donazzolo, R., Guidi, F., Orio, A.A. 1991. Heavy metal pollution studies by 
resuspension experiments in Venice Lagoon. Water Res. 25:1295-1302. 

Cantwell, M.G., Burgess, R.M., Kester, D.R. 2002. Mobilization and phase partitioning of metals 
from anoxic estuarine sediments during periods of simulated resuspension. (In preparation.) 


131 


Carey, J., Cook, P., Giesy, J., Hodson, P., Muir, D., Owens, W., Solomon, K., eds. 1998. 
Ecotoxicological Risk Assessment of Chlorinated Organic Chemicals. SETAC Press, Pensacola, 
FL. 375 pp. 

Cody, T.E., Radike, M.J., Warshawsky, D. 1984. The phototoxicity of benzy[a]pyrene in the 
green alga Selenastrum capricornutum. Environ. Res. 35:122-132. 

Cook, P.M.., Carlson, A.R., Lee, H, II. 1992. Tissue residue approach. In Sediment Classification 
Methods Compendium, Chapter 7. EPA 823-R-92-006. EPA, Office of Water, Washington, DC. 

Cook, P.M.., Zabel, E.W., Peterson, R.E. 1997. The TCDD toxicity equivalence approach for 
characterizing risks for early life stage mortality in trout. In Rolland, R., Gilbertson, M., 

Peterson, R., eds., Chemically-Induced Alterations in the Functional Development and 
Reproduction of Fishes. SETAC Press, Pensacola, FL, pp. 9-27. 

Crane, M., Newman, M.C., Chapman, P.F., and Fenlon J. 2001. Risk Assessment with Time to 
Event Models. Lewis Publishers, Boca Raton, FL. 175 pp. 

Di Toro, D.M., Hansen, D.J., McGrath, J.A., Beny, WJ. 2002. Predicting the toxicity of metals 
in sediments using organic carbon normalized SEM and A VS. (In preparation.) 

Erickson, R., Kleiner, C., Fiandt, J., Highland, T. 1989. Feasibility of predicting the effects of 
fluctuating concentrations on aquatic organisms and possible application to water quality criteria. 
EPA-600/X-89-307. EPA, Duluth, MN. 

Erickson, R.J., Ankley, G.T., DeFoe, D.L., Kosian, P.A., Makynen, E.A. 1999. Additive toxicity 
of binary mixtures of phototoxic polycyclic aromatic hydrocarbons to the oligochaete 
Lumbriculus variegatus. Toxicol. Appl. Pharmacol. 154:97-105. 

EPA. 1973. Water quality criteria 1972. A report of the Committee on Water Quality Criteria, 
Environmental Studies Board, National Academy of Sciences, National Academy of 
Engineering. EPA-R3-73-033. Washington, DC. 

EPA. 1980. Guidelines for deriving water quality criteria for the protection of aquatic life and its 
uses. Fed. Reg. 45:79341-79350. 

EPA. 1991. Technical support document for water quality-based control. EPA/505/2-90-001. 
Office of Water, Washington, DC. 

EPA. 1992. Framework for ecological risk assessment. EPA/630/R-92/001. Risk Assessment 
Forum, Washington, DC. 

EPA. 1994. Interim guidance on determination and use of water-effect ratios for metals. EPA 
823-B-94-001. Office of Water, Washington, DC. 

EPA. 1995a. Final water quality guidance for the Great Lakes system, final rule. Fed. Reg. 60. 


132 


EPA. 1995b. Great Lakes water quality initiative technical support document for the procedure to 
determine bioaccumulation factors. EPA-820-B-95-005. Office of Water. NTIS PB95187290. 

EPA. 1997. Mercury study report to Congress-Volume VI: an ecological assessment for 
anthropogenic mercury emissions in the United States. EPA-452/R-97-008. Office of Air Quality 
Planning and Standards and Office of Research and Development, Washington, DC. 

EPA. 1998. Guidelines for ecological risk assessment. EPA/630/R-95/002F. Risk Assessment 
Forum, Washington, DC. 

EPA. 1999. 1999 update of ambient water quality criteria for ammonia. Office of Water, 
Washington, DC. 

EPA. 2000a. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic 
organisms: metal mixtures (cadmium, copper, lead, nickel, silver, and zinc). EPA-822-R-00-005. 
Office of Science and Technology and Office of Research and Development, Washington, DC. 

EPA. 2000b. Technical basis for the derivation of equilibrium partitioning sediment guidelines 
(ESGs) for the protection of benthic organisms: nonionic organics. EPA-822-R-00-001. Office of 
Science and Technology and Office of Research and Development, Washington, DC. 

EPA. 2000c. Methods for the derivation of site-specific equilibrium partitioning sediment 
guidelines (ESGs) for the protection of benthic organisms: nonionic organics. 
EPA-822-R-00-002. Office of Science and Technology and Office of Research and 
Development, Washington, DC. 

EPA. 2000d. Equilibrium partitioning sediment guidelines (ESGs) for the protection of benthic 
organisms: PAH mixtures. Draft report. Office of Science and Technology and Office of 
Research and Development, Washington, DC. 

EPA. 2000e. Wildlife research strategy. NHEERL/ORD. September. 

EPA. 2000f. Methodology for deriving ambient water quality criteria for the protection of human 
health. EPA-822-B-00-004. Office of Science and Technology, Washington, DC. 

EPA. 2001. Workshop report on the application of 2,3,7,8-TCDD toxicity equivalence factors to 
fish and wildlife. Risk Assessment Forum, Washington, DC. 

EPA. 2002. Framework for application of the toxicity equivalence methodology for 
polychlorinated dioxins, furans, and biphenyls in ecological risk assessments. Office of Research 
and Development, Washington, DC. (in prep.) 

Farag, A.M., Boese, C.J., Woodward, D.F., Bergman, H.L. 1994. Physiological changes and 
tissue metal accumulation in rainbow trout exposed to foodbome and waterborne metals. 

Environ. Toxicol. Chem. 13:2021-2029. 


133 


Farag, A.M., Woodward, D J., Brumbaugh, W., Goldstein, J.N., MacConnell, E., Hogstrand, C. 
1999. Dietary effects of metals-contaminated invertebrates from the Coeur d’Alene River, Idaho, 
on cutthroat trout. Trans. Amer. Fish. Soc. (in press). 

Gobas, F.A.P.C. 1993. A model for predicting the bioaccumulation of hydrophobic organic 
chemicals in aquatic food-webs: application to Lake Ontario. Ecol. Model. 69:1-17. 

Guiney, P.D., Cook, P.M., Casselman, J.M., Fitzsimons, J.D., Simonin, H.A., Zabel, E.W., 
Peterson, R.E. 1996. Assessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin induced sac fry 
mortality in lake trout (Salvelinus namaycush) from different regions of the Great Lakes. Can. J. 
Fish. Aquat. Sci. 53:2080-2092. 

Gustafsson, O., Gschwend, P.M. 1997. Soot as a strong partition medium for polycyclic aromatic 
hydrocarbons in aquatic systems. In Eganhouse, R.P., ed.. Molecular Markers in Environmental 
Geochemistry. American Chemical Society, Orlando, FL, pp. 365-381. 

Hall, L.W. Jr., Scott, M.C., Killen, W.D. 1998. Ecological risk assessment of copper and 
cadmium in surface waters of Chesapeake Bay watershed. Environ. Toxicol. Chem. 17:1172- 
1189. 

Hickie, B.E., McCarty, L.S., Dixon, D.G. 1995. A residue-based toxicokinetic model for pulse- 
exposure toxicity in aquatic systems. Environ. Toxicol. Chem. 14:2187-2197. 

Holst, L.L., Giesey, J.P. 1989. Chronic effects of the photoenhanced toxicity of anthracene on 
Daphnia magna reproduction. Environ. Toxicol. Chem. 8:933-942. 

Hook, S.E., Fisher, N.S. 2000. Sublethal toxicity of metals to marine copepods following dietary 
exposure: a consistent pattern emerges. Abstracts., 21st Annual Meeting of Society of 
Environmental Toxicology and Chemistry. 

Hook, S.E., Fisher, N.S. 2001. Sublethal effects of silver in zooplankton: importance of exposure 
pathways and implications for toxicity testing. Environ. Toxicol. Chem. 20:568-574. 

Huang, X.D., Dixon, D.G., Greenberg, B.M. 1993. Impacts of UV radiation and 
photomodification on the toxicity of PAHs to the higher plant Lemna gibba (duckweed). 

Environ. Toxicol. Chem. 12:1067-1077. 

Jones, J.R.E. 1964. Fish and River Pollution. Butterworths, London. 203 pp. 

Kagan, J., Kagan, P.A., Buhse, H.E. Jr. 1984. Light-dependent toxicity of a-terthienyl and 
anthracene toward late embryonic stages of Rana pipiens. J. Chem. Ecol. 10:1115-1122. 

Kagan, J., Kagan, E.D., Kagan, I.A., Kagan, P.A., Quigley, S. 1985. The phototoxicity of non- 
carcinogenic polycyclic aromatic hydrocarbons in aquatic organisms. Chemosphere 14:1829- 
1834. 


134 


Karickhoff, S.W., Morris, K.R. 1985a. Sorption dynamics of hydrophobic pollutants in sediment 
suspensions. Environ. Toxicol. Chem. 4:469-479. 

Karickhoff, S.W., Morris, K.R. 1985b. Impact of tubificid oligochaetes on pollutant transport in 
bottom sediments. £«v/row. <Sc/. Technol. 19:51-56. 

Kim, S.D., Metzler, D.M., Cha, D.K., Allen, H.E. 2000. Is food-borne cadmium more 
bioavailable than sorbed/water-bome cadmium? Abstracts, 21 st Annual Meeting of Society of 
Environmental Toxicology and Chemistry. 

Kooijman, S.A.L.M. 1987. A safety factor for LC50 values allowing for differences in sensitivity 
among species. Water Res. 21:269-276. 

Latimer, J.S., Davis, W.R., Keith, D.J. 1999. Mobilization of PAHs and PCBs from in-place 
contaminated marine sediments during simulated resuspension events. Estuar. Coast. Shelf Sci. 
49:577-595. 

Lee, G., Ellersieck, M.R., Krause, G.F., Mayer, F.L. 1995. Predicting chronic toxicity of 
chemicals to fishes from acute toxicity test data: multifactor probit analysis. Environ. Toxicol. 
Chem. 14:345-349. 

Leonard, E.N., Mattson, V.R., Benoit, D.A., Hoke, R., Ankley, G.T. 1993. Seasonal variation of 
acid volatile sulfide in sediment cores from three northeastern Minnesota lakes. Hydrobiologia 
271:87-95. 

Mancini, J.L. 1983. A method for calculating effects on aquatic organisms of time-varying 
concentrations. Water Res. 17:1355-1361. 

Mayer, F.L. 1987. Acute toxicity handbook of chemicals to estuarine organisms. EPA/600/8- 
87/017. EPA, Gulf Breeze, FL. 274 pp. 

Mayer, F.L., Ellersieck, M.R. 1986. Manual of acute toxicity: interpretation and data base for 410 
chemicals and 66 species of freshwater animals. Resource Publ. 160. U.S. Fish and Wildlife 
Service, Washington, DC. 579 pp. 

Mayer, F.L., Krause, G.F., Buckler, D.R., Ellersieck, M.R., Lee, G. 1994. Predicting chronic 
lethality of chemicals to fishes from acute toxicity test data: concept and linear regression. 
Environ. Toxicol. Chem. 13:671-678. 

Meyer, J.S., Gulley, D.D., Goodrich, M.S., Szmania, D.C., Brooks, A.S. 1995. Modeling toxicity 
due to intermittent exposure of rainbow trout and common shiners to monochloramine. Environ. 
Toxicol. Chem. 14:165-175. 

Mount, D.R., Barth, A.K., Garrison, T.D., Barten, K.A., Hockett, J.R. 1994. Dietary and 
waterborne exposure of rainbow trout (Oncorhynchus mykiss) to copper, cadmium, lead, and zinc 
using a live diet. Environ. Toxicol. Chem. 13:2031-2041. 


135 


Mount, D.R., Diamond, S.A., Erickson, R.J., Simcik, M.F., Swackhamer, D.L. 2001. Linking 
exposure and dosimetry to risk from photo-activated toxicity of PAHs. Presentation, 22nd 
Annual Meeting of the Society of Environmental Toxicology and Chemistry, Baltimore, 
Maryland. 

Neff, J.M. 1979. Polycyclic Aromatic Hydrocarbons in the Aquatic Environment: Sources, Fates, 
and Biological Effects. Applied Science Publishers, London. 

Newman, M.C. 1995. Quantitative Methods in Aquatic Ecotoxicology. Lewis Publishers, Boca 
Raton. 

Newman, M.C., Ownby, D.R., Laurent, C.A.M., Powell, D.C., Christensen, T.R.L., Lerberg, 

S.B., Anderson, B.-A. 2000. Applying species-sensitivity distributions in ecological risk 
assessment: assumptions of distribution type and sufficient numbers of species. Environ. Toxicol 
Chem. 19:508-515. 

Newsted, J.L., Giesy, J.P. 1987. Predictive models for photoinduced acute toxicity of polycyclic 
aromatic hydrocarbons to Daphnia magna, Strauss (cladocera, Crustacea). Environ. Toxicol 
Chem. 6:445-461. 

Nichols, J.W., Jensen, K.M., Tietge, J.E., Johnson, R.D. 1997. A physiologically-based 
toxicokinetic model for maternal transfer of 2,3,7,8-tetrachlorodibenzo-p-dioxin in brook trout 
(Salvelinusfontinalis). Environ. Toxicol Chem. 17:2422-2434. 

Oris, J.T., Giesy, J.P. 1985. The photoenhanced toxicity of anthracene to juvenile sunfish 
(Lepomis spp.). Aqimt. Toxicol. 6:133-146. 

Oris, J.T., Giesy, J.P. 1987. The photo-induced toxicity of polycyclic aromatic hydrocarbons to 
larvae of the fathead minnow {Pimephales promelas). Chemosphere 16:1395-1404. 

Oris, J.T., Hatch, A.C., Weinstein, J.E., Findlay, R.H., McGinn, P.J., Diamond, S.A., Garrett, R., 
Jackson, W., Burton, G.A., Allen, B. 1998. Toxicity of ambient levels of motorized watercraft 
emissions to fish and zooplankton in Lake Tahoe, Califomia/Nevada, USA. Presentation, 8th 
Annual Meeting of the European Society of Environmental Toxicology and Chemistry, 

Bordeaux, France. 

Simpson, S.L., Apte, S.C., Batley, G.E. 1998. Effect of short-term resuspension events on trace 
metal speciation in polluted anoxic sediments. Environ. Sci. Technol. 32:620-625. 

Solomon, K.R., Baker, D.B., Richards, R.P., Dixon, K.R., Klaine, S.J., LaPoint, T.W., Kendall, 
R.J., Weiskopf, C.P., Giddings, J.M., Giesy, J.P., Hall, L.W. Jr., Williams, W.M. 1996. 

Ecological risk assessment of atrazine in North American surface waters. Environ. Toxicol 
Chem. 15:31-76. 


136 


Stephan, C.E., Mount, D.I., Hansen, D.J., Gentile, J.H., Chapman, G.A., Brungs, W.E. 1985. 
Guidelines for deriving numerical national water quality criteria for the protection of aquatic 
organisms and their uses. PB85-227049. NTIS, Springfield, VA. 

Sun, K., Krause, GJF., Mayer, F.L., Ellersieck, M.R., Basu, A.P. 1995. Predicting chronic 
lethality of chemicals to fishes from acute toxicity data: theory of accelerated life testing. 
Environ. Toxicol. Chem. 14:1745-1752. 

Thomann, R.V., Connolly, J.P., Parkerton, T.F. 1992. An equilibrium model of organic chemical 
accumulation in aquatic food webs with sediment interaction. Environ. Toxicol. ChemAl’.6\S- 
629. 

Tilghman Hall, A., Oris, J.T. 1991. Anthracene reduces reproductive potential and is maternally 
transferred during long-term exposures to fathead minnow. Aquat. Toxicol. 19:249-264. 

Wagner, C., Lokke, H. 1991. Estimation of ecotoxicological protection levels from NOEC 
toxicity data. Water Res. 25:1237-1242. 

Weber, C.I. 1993. Methods for measuring the acute toxicity of effluents to freshwater and marine 
organisms. EPA/600/4-90/027F. EPA, Environmental Monitoring Systems Laboratory, 
Cincinnati, OH. 

Woodward, D.F., Brumbaugh, W.G., DeLonay, A.J., Little, E.E., Smith, C.E. 1994. Effects on 
rainbow trout fry of a metals-contaminated diet of benthic invertebrates from the Claric Fork 
River, Montana. Trans. Amer. Fish. Soc. 123:51-62. 

Woodward, D.F., Farag, A.M., Bergman, H.L., DeLonay, A.J., Little, E.E., Smith, C.E., Barrows, 
F.T. 1995. Metals-contaminated benthic invertebrates in the Clark Fork River, Mc»itana: effects 
on age-0 brown trout and rainbow trout. Can. J. Fish. Aquat. Sci. 52:1994-2004. 


137 


Section 8. 

Implementation Plan for Diagnostics Research 


Problem 

States list surface waters as impaired on 305(b) reports or 303(d) listings based on one or more of 
three types of criteria: biological criteria (narrative or numeric), chemical criteria, or physical 
attributes (e.g., habitat quality assessments). When impairment is determined based on 
biological criteria (26% of impairment decisions). States are faced with the problem of 
diagnosing the cause of impairment before plans can be made to reduce the loading of pollutants 
through the TMDL process (40 CFR Ch.l, Part 130; U.S. EPA 1991; 
httD://www.epa.gov/owow/tiTidl/l . The nation-wide scope of this problem is enormous; 
approximately 21,000 water bodies have been designated as impaired; or 44% of stream or river 
miles, 49% of lakes, reservoirs, and ponds; 98% of Great Lakes shoreline waters; and 42% of 
estuaries (EPA 2000a). 

To improve overall efficiency of the TMDL process and to coordinate remediation activities, 
diagnosis of the cause of impairment is needed not only at the scale of individual water bodies, 
but also at the watershed scale. Unified Watershed Assessments, as specified in the Clean Water 
Action Plan (EPA 1998a) are needed at the watershed scale to identify aquatic systems for 
restoration actions (EPA 1999, Federal Register 2000, http://www.epa.gov/owow/uwaL 

Overall vision 

This plan for diagnostics research provides a comprehensive and integrated approach for problem 
solving. It is primarily responsive to EPA’s regulatory and management needs, in particular the 
need for research related to the TMDL program, but also supports needs defined under 
Superfund, National Pollution Discharge Elimination System (NPDES), site remediations, and 
other relevant activities (e.g., FWS Natural Resource Damage Assessments). It provides a 
conceptual framework to determine current and future research priorities coordinated across 
NHEERL, and also discusses implementation in the context of research and expertise provided 
by other ORD Laboratories and the broader scientific community. Outputs from diagnostics 
research will be incorporated into a series of decision-support systems or modules for use by 
clients (EPA Regions, States, Tribes, and Program Offices). 

NHEERL’s diagnostic research focuses on the need to diagnose causes of biological impairment 
within an integrated framework linking watersheds with receiving water bodies to support the 
TMDL process and other regulatory programs. All stressors (habitat alteration, nutrients, 
suspended and bedded sediments, and toxic chemicals) will be considered under diagnostics 
research; however, greater emphasis may be placed on an individual stressor, combinations of 
stressors, and/or modes of action according to the prevailing problems or issues of a habitat, 
water body, ecosystem, region, or the nation as a whole. The starting point for diagnostic 
research is the need to respond to reports of biological impairment, nonattainment of aquatic life 
use, and other indications of adverse effects (e.g., toxicity). Initial assessments also can record 
evidence of multiple potential causes of impairment and conflicting lines of evidence that might 


138 




complicate a diagnosis. Thus, the endpoint for the diagnostic process includes both the definition 
of the primary causes of impairment as well as the allocation of observed effects among multiple 
potential stressors, and the assessment of potential interactive effects among stressors. 

To narrow the number of realistic stressors of concern, an approach based on the Toxicity 
Identification Evaluation (TIE) procedures will be developed. In the TIE process, toxic 
chemicals are first considered in broad classes. As the evaluation proceeds, the focus moves 
towards specific chemicals. In this way, large numbers of insignificant chemicals are excluded 
from further evaluation. For example in a sediment TIE, sediment may be classified as toxic due 
to organic chemicals, then narrowed to pesticides, and finally to DDT. Analogously, in 
diagnosing causes of impairment, an approach will be developed which starts with broad stressor 
classes (i.e., habitat alteration, nutrients, suspended and bedded sediments and toxic chemicals), 
then unimportant stressors will be disregarded, and ultimately, a specific stressor(s) will be 
selected as the cause of impairment. 

In developing this plan, we considered and evaluated the States’ implementation stages from 
monitoring through diagnosis to restoration. Implementation stages were then linked to 
associated uncertainties, research needs, and desired research products. From these efforts, 

APGs, their accompanying APMs, and the critical path for diagnostic research were developed, 
and are presented in the next two subsections. The evolution of a combined TMDL/Restoration 
Path from the current parallel paths for State/Tribal assessments, TMDL, and watershed 
restoration processes is described below. 

Goals 

There are four primary goals for this diagnostics research: 

• Provide a framework for interpreting cause-and-effect relationships, including: 

• Conceptual ecosystem models based on appropriate mechanisms of action that can 
be used to improve the accuracy of impairment decisions; 

• Conceptual models to define ecosystem and watershed natural conditions and 
driving factors to use as a basis to quantify degree of impairment and to set 
restoration goals; and 

• Classification frameworks that explain variation in the response of individuals, 
populations, communities, and ecosystems at regional, watershed, water body, and 
habitat scales. 

• Develop single-stressor diagnostic methods and models to determine the primary source 
of biological impairment of aquatic ecosystems. 

• Develop methods and models to allocate causality among multiple stressors and/or to 
diagnose interactions among them. 


139 


• Develop methods and models capable of forecasting causality to evaluate the ecological 
benefits of source reductions, to investigate stressor interactions, and to assess the gains 
and losses realized by various alternatives for restoration and remediation. 

Ancillary goals are to improve the state-of-the-science of monitoring and assessment in support 
of diagnostic methods, and to provide clients with diagnostic tools in user-friendly interfaces. 
Tools with different levels of accuracy and sophistication are needed within the program 
depending on cost-benefit ratios of decision making. Accordingly, the tools currently presented 
in this research plan range from simple screening tools (watershed classification schemes) to 
those of intermediate complexity (e.g., development of diagnostic community-scale indicators) to 
those of even greater complexity (e.g., use of linked mass-balance and food web models for Lake 
Michigan regional case study). Decision-support systems will be developed to incorporate all of 
these features as a format for technical transfer to ORD’s clients. Conceptual model 
development will provide a general framework for decision-support systems, which will then be 
regionalized based on classification systems developed to explain differences in system behavior 
(e.g., stressor-response relationships). Tools for diagnosing both single-stressor impacts and 
multiple stressor interactions will be piloted using regional case studies. These pilots will then 
be incorporated as example applications into decision-support systems. Ultimately, the decision- 
support systems will be linked to tools developed by NRMRL, forecasting not only future 
impacts based on no action, but also the results of alternative remediation scenarios. 

Annual Performance Goals have been derived by defining five implementation stages that the 
States must go through between monitoring and diagnosis of the causes of impairment 
(Appendix 1). Within each implementation phase, tasks that the States need to perform are 
defined, along with their associated uncertainties. These defined tasks are then used to derive 
related NHEERL research needs. Finally, research and technical transfer products are linked 
with these tasks, uncertainties, and research areas, and research and technical transfer products 
are identified as APMs associated with each APG. The time line for implementation of APMs is 
shown below, with APMs grouped by APGs. 

APG 1 FY03 (GPRA # 16) Provide the scientific foundation and information management 
scheme for the 303(d) listing process including a classification framework for surface waters, 
watersheds, and regions to guide problem formulation. 

APM 1A FY02 Conceptual framework for both single and multiple stressors including a 
consideration of cross-scale issues (AED, MED). 

APM IB FY03 (GPRA # 202) Classification frameworks for geographic regions and at 
the watershed, water body and habitat scale (MED, GED). 

APG 2 FY05 Provide first generation diagnostic methods, including stressor identification (SI) 
methods, for causal linkage of observed major classes of single stressors and biological 
indicators to stressors in freshwater and marine systems; scale the methods to States and 
watershed organizations. 

APM 2A FY03 Guidance on whole sediment TIE procedures (MED, AED). 


140 


APM 2B FY05 Application of coastal watershed and estuarine/lacustuary classification 
schemes to predict probability of impairment based on Great Lakes and Gulf of Mexico 
regional case studies (GED, MED). 

APM 2C FY05 Guidance on and user-friendly interfaces for derivation of diagnostic 
indicators for individual stressors (MED, AED). 

APG 3 FY07 Provide diagnostic methods and technical support documents for determining the 
relative significance of multiple stressors in 303(d) listed waters. 

APM 3A FY02 Case studies of multivariate approaches to community data analysis to 
apportion cause among stressors. (AED, MED). 

APM 3B FY06 Simulation of key stressor interactions with generic ecosystem models 
using sensitivity analysis to define the range of stressors and stressor combinations under 
which nonadditive interactive effects will occur (MED, AED). 

APM 3C FY07 Decision-support system(s), including forecasting of future cause-effect 
relationships (MED). 

Critical Path 

The relationship of the diagnostic APGs to each other and to the research of other ORD 
laboratories (NERL, NRMRL) is defined in the critical path diagram (Figure 12). The steps in 
the critical path are described as follows: 

Step 1. Develop a conceptual framework (APG 1 FY03). 

This APG includes the development of conceptual models illustrating stressor-response 
relationships for single and multiple stressors and development of appropriate classification 
frameworks at the habitat, water body, watershed, and regional scales. Development of 
hierarchical classification frameworks involves the determination of which types of habitats, 
water bodies, watersheds, and regions are expected to behave similarly in response to a given 
level of stressor or loading. Thus, classification helps establish regional, watershed, or habitat- 
specific criteria and the range across which model extrapolations (including empirical stressor- 
response curves) are appropriate. At this stage, the nature of significant interactions among 
stressors also will be defined based on the expected modes of action. 

Step 2. Development of single-stressor methods and models (APG 2 FY05). 

Methods and models for diagnosis of the predominant source of impairment from single stressors 
are needed. Significant input is required at this stage from other research areas (Sections 4-7). 

Step 3. Development of multiple-stressor methods and models (APG 3 FY07). 


141 



(NR ML) 


Figure 12. Critical path (flow of APGs) for diagnostics research. 


142 






















Methods and models for single stressors are combined and refined to diagnose multiple sources 
of impairment. The latter stage includes the development of tools both to allocate cause among 
multiple additive stressors and to diagnose significant interactive effects among stressors. The 
first stage in diagnosing significant stressor interactions will involve the use of generic ecosystem 
models to perform sensitivity analyses to determine the probability of observing significant 
interactions among stressor classes over realistic ranges of loadings or stressor levels (e.g., 

Bartell et al. 1984, Mitsch and Reeder 1991, Hanratty and Stay 1994, EPA 2000b). Ultimately 
these tools will be incorporated into a decision-support system. 

Step 4. Develop forecasting approaches. 

This step builds upon the development of multi-stressor methods and models to include 
forecasting techniques to project the response of aquatic ecosystems to load reductions and/or 
watershed restoration activities into the future. Forecasting methods will be particularly 
important in protecting large, complex, unique resources (e.g., Great Lakes, Gulf of Mexico, 
Chesapeake Bay) for which costs of restoration are large, interactions are involved, and lag times 
between an event and the eventual system response must be taken into consideration. 
Development of forecasting techniques will also allow NHEERL to be proactive in defining 
potential shifts in causes of impairment, and to anticipate future threats to the environment. 
Activities in this area will be coordinated with NRMRL. 

Outputs from this research path will require collaborative efforts with NERL to develop 
improved loading models for TMDLs that include components predicting biological respcmses, 
and the development of appropriate exposure metrics to improve monitoring designs. In 
addition, classification frameworks and other tools developed here will be coordinated with 
research on prioritization of watershed restoration activities, prediction of recovery paths, and 
assessment of the success of remediation actions currently under way within NRMRL. 

Methods and models developed under diagnostics research also will feed into the diagnostic logic 
flow sequence described in the EPA SI document for analysis of data for a weight-of-evidence 
approach (EPA 2000c). Potential points of influence on the SI overall process are presented in 
Figure 13. 

In Figure 14, the research products (APMs) from the critical path are connected to the 
State/Tribal implementation stages for both the TMDL and watershed restoration processes for 
impaired surface waters, and then merged into an integrated process. The first APG, 
development of a conceptual framework, feeds directly into the problem formulation aspect of 
the diagnostic process and provides the basis for developing a decision-support system for the 
diagnostic process. The second APG connects directly to diagnosing the primary cause of 
impairment. The third APG provides research products which support three areas of the 
combined TMDL/Watershed Restoration path: Diagnostic and Condition-based Monitoring and 
Assessment, Allocation of Causes and Interactions among Multiple Stressors, and Confirmation 
of Diagnosis with Uncertainty Evaluation. The fourth goal supports diagnostic model 
development for forecasting and predictive approaches for the evaluation of remediation options. 


143 



Figure 13. A logic for characterizing the causes of ecological injuries at specific sites. 
Modified from Figure 4-1 in SI document (EPA 2000c) to show potential inputs from aquatic 
stressors diagnostics research. 


144 














































<A 

O 

3 

T3 

O 


£ 

o 

w 

(0 

v 

(A 

a> 


oc 

lU 

UJ 

X 

z 


-I 

<0 

O M 
C d) 
CD M 
<0 « 
=5 ™ 

~ <0 
« O) 

o i 

e- o 

8 1 

c o 

Z E 

O <]) 

11 

ii 

<0 .5 


0) 

■g 

■> 

p 


c» 

c 


CO 

o 

CO 

•D 

c 

(D 


CO 

o 

CO 

0) 

iS 

CO 

c 

‘c 

o 

Q. 

a 

3 

<0 0) 
o 
</) 
(0 
a> 
o 
o 


(0 

o 


CO 

o 

CO 

c 

•i i 

m 

§3 

o E 


1 ^ 

(0 d) 

•S -Q 

1 2 

5 a 

d) >- 

E o 

2 d) 

r E 

(0 d> 

3 x: 
o o 

8 E 


O S 

I s 

O « 
<D C 

E 2 

<Q 0) 


O 

*0 

*> 

p 


a> 

a> 

(0 

c 

<0 

E 


C ■D 

•B S 
S « 

"O 

■« s 

0) <5 

c to 
T) W 
3 (0 

O o 
c « 
■” "D 
in c 
tn to 

§- 

2 o 

it w 
o *- 
c o 




I- 
1 § 
B 3 

8 0) 

<0 

C 3 

■g a 

“1 

CO a> 

CD d) 


S < -D 


9 


(0 

0. 


o 

r: 


o 

(0 

0) 


o 

*D 

O 

E 

■D 

C 


o 

_ </) 
«s 


0) (0 

It 


(0 

c 

4) 


o 

Q. 

0) 

c» g 

0) .2 


"S 

3 

3 

*- X 

C o 
o c 

E 

o 

<0 

> 

o 

in Q 
a c 

(0 

"O 

c 


0) 

<c 

2 E 

a> ® 

*D 

O 

o 

</) 

LL 

E 

3 

^ 8 

•Q 

s 

’r E 

C 

(Q 

o 

® d) 

<0 

c 

£ -c 
o ® 

•D 

O 

o 

E - 
S X 

2 o 

O 

E 

IS 

o 

o 


■I B 8 o 

tn ^ 


in 
O 

= !2 
°» ® 

T3 _to 
d> O 

•o ._ 

> O 

P to 

0. E 


o C 


to i 

1 I 

c» o 

o8 o 

in “= 

9? ®- 

< in 


B 5 § 

8 I c 

M O 
T3 
d) 

■g 
■> 
o 


S 

d) 
c 

<0 — 

o o 
(/) <0 
(0 (0 
_ 2 2 
0. CO 


<0 O 

<0 

° I 

C3 E 
10 « 
ri © 

o w 
c ® 
o 


~ o -e w 

m St 


E 8 

g> 
c <0 
o o 
o *D 

is o> 
O c 

■S 2 

o c 
£ o 

E ^ 

^ O) 
<D C 
2 TJ 
> 3 

2 o 
0- £ 


(0 
c 
<0 

>» 

4-» 

_c 
10 
•C w 
® c 

s ® 


E 

3 

O 

o 


ID 

2 

o 

e- 

§ «> 

.£ (D 


O % 


c 

3 

■o 

c ^ 

<0 -Q 

1 § 

§1 
in 3 
c O) 

B CO 
10 

2 2 
w Q. 

£ < 


* Q. 
W Q. 
© <0 
"O d> 
O > 

.2 ^ 

« 2 

I - 

^ C 

I s 

> d) 
® ^ 
a S 



c o 

.2 M 

•s ® 

.2 ® 

*0 ® M 

d> iS c 
E U 5 

a o ^ 

in 

_i c 
o o 

O 


(0 


c 


o 

JZ 

O) 

ro 

0) 

0. 

X 

c 

g 

c5 

m 

0) 

k. 

o 

c 

w 

0) 

0) 

w 

k. 

X 

3 


o 




c 

o 


® u> 
© <0 

■o a 

d> “■ 
£ 
in 


d> 

(0 

$ 


145 


Figure 14. Relationship between current stages of State/Tribal assessment, TMDL and watershed restoration planning processes, and 
proposed combined path. 































Research Projects 


The research projects proposed here establish a conceptual framework to guide implementation 
of diagnostics, provide case studies to develop and test methods and models for both single-and 
multiple-stressor scenarios, assess the likelihood of multiple stressor interactions, and establish 
the structure for a decision-support system. Opportunities for interlaboratory collaboration on 
this research are shown in Appendix 2. 

Project Title 1. Conceptual Model Development and Information Management Framework 

Project Coordination and Resources (6.5 FTEs: FY02: AED, GED, MED [total = 6.0]; FY03: 
MED-0.25, AED-0.25 [total = 0.5]) 

Objectives 

The goal of this project is to support the problem formulation stage in diagnostics (EPA 1996). 
The main objective is to develop conceptual models describing stressor-response relationships 
within ecosystems, including potential interactions among multiple stressors across all scales 
relevant to setting a protective TMDL (EPA 1996). These conceptual models will then provide 
the basis for creating a national database on nontoxic aquatic stressor-response relationships and 
for improving information management systems in support of 303(d) assessment activities. 

Scientific Approach 

Conceptual model development will focus on the effects of habitat alteration, nutrients, 
suspended and bedded sediments, and toxic chemicals on appropriate endpoints (individuals, 
populations, communities, ecosystems) across spatial scales (habitats, water body, watershed, 
region) relevant to setting a protective TMDL. This research will be coordinated across all of the 
Ecology Divisions and across all five research areas (Sections 4-7). Priorities for refining 
conceptual models for single and multiple stressors at the habitat scale will be established by 
examining the relative frequency of stressor X or stressor combination X, ,Xj... with habitat type 
Y combinations in the OW 303(d) listing database (EPA 2001, 

http://www.epa.gov/owow/tmdl/trcksvs.htiTih . A determination of impairment may be controlled 
by different factors as the scale of impact gets larger. Therefore, conceptual models will examine 
the cross-scale interactions (habitat <=> water-body <=> watershed <=>region) that must be 
understood to determine the appropriate scale at which a protective TMDL must be established. 
In addition, the interactions among predominant stressors will be included as appropriate, based 
on expected mechanisms of action. Conceptual models will be developed through one or more 
cross-divisional workshops. 

Two types of information management frameworks will be developed in support of the 303(d) 
listing process and diagnosis of the causes of impairments. First, a framework for supplying 
existing geospatial information for the problem formulation stage will be established. To 
identify potential causes of impairment, this framework will build upon database networks 
currently under development or refinement by OW, including geospatial databases incorporated 
within the Better Assessment Science Interacting Point and Nonpoint Source (BASINS) 


146 



modeling support system developed by EPA’s OW/Office of Science and Technology (OST) 
(http://www.eDa.gov/ost/basinsA EPA 1998b), STORAGE and RETRIEVAL database 
(STORET), the Water Quality Standards Database, the TMDL tracking database, and Watershed 
Assessment Tracking and Environmental Results Systems (WATERS). In addition, 
incorporation of toxicity data into EPA’s STORET database will be coordinated with this effort. 

In siqDport of diagnostic efforts at the watershed scale, NHEERL will continue to collaborate with 
EPA’s Office of Environmental Information (OEI) and USGS Mapping Division (EROS Data 
Center) and Water Division in their work to produce a seamless nationwide geospatial database 
of watershed boundaries and associated hydrological derivatives [NED-H (now EDNA, 

Elevation Derivatives for Natural Applications), see 

http://edcntsl2.cr.usgs.gov/ned-h/index.html . Protocols for deriving watershed boundaries have 
been developed through an interagency task force coordinated by the Federal Geographic Data 
Committee (FGDC) and Advisory Committee on Water Information (ACWI), thus ensuring 
consistency across Federal agencies (see Federal Standards for Delineation of Hydrologic Unit 
Boundaries, 06/12/01 Draft; http://www.ftw.nrcs.usda.gov/huc data.html ). The USGS 8-digit 
hydrologic unit codes (HUCs) are being divided into finer units (10- and 12-digit HUCs) that are 
generally consistent with watershed boundaries for integrated drainages, and with boundaries for 
internal drainages to moderately-sized water bodies. The smallest of these units (12-digit HUCs) 
correspond to the scale of watersheds associated with wadeable streams. NHEERL is supporting 
development of GIS tools for automated watershed delineation using digital elevation models 
(DEMs), and hydrologic correction of existing DEMs to ensure consistency between synthetic 
streamlines and mapped hydrography. ThuSj attributes coded using the National Hydrography 
Database for streams (the successor to EPAs Reach 3 stream files) will be consistent with the 
Nationwide Watershed Boundary Dataset under development. Regional case studies (project 3) 
will provide an opportunity to use the Nationwide Watershed Boundary Database under 
development to demonstrate its usefulness in watershed-scale monitoring designs, assessments, 
diagnosis, and management across an integrated series of watershed scales. 

Second, a nonpoint-stressor analog to the Ecological Toxicity Database (ECOTOX) 
(http://www.epa.gov/ecotox/. Hunter et al. 1990) will be developed that will contain information 
on stressor-response relationships for nontoxics, stratified by an appropriate classification 
framework (project 2). ECOTOX is a source for locating single chemical toxicity data for 
aquatic life, terrestrial plants and wildlife. ECOTOX integrates three toxicology effects 
databases: AQUIRE, terrestrial plants (PHYTOTOX), and terrestrial wildlife (TERRETOX). 
Toxicity test results and related testing information for any individual chemical from laboratory 
and field aquatic toxicity tests are extracted from the literature and recorded. Lethal, sublethal, 
and bioconcentration effects are recorded for freshwater and marine organisms, along with 
pertinent information on laboratory or field test conditions. The current database structure could 
be adapted readily to store information on stressor-response relationships for non-toxics. These 
data would ultimately support the development of regional and/or national criteria for nutrients 
and suspended and bedded sediments. 


147 






Products 


APM lA FY02 Conceptual framework including consideration of both single and multiple 
stressors and cross-scale issues (AED, MED). 

Database structure to support problem formulation in the diagnostic process (303(d) listings) and 
Nation-wide database for stressor-response relationships for non-point source stressors (all 
Ecology Divisions, FY03). 

Benefit of Products 

One of the difficulties in diagnosing the causes of impairment is the lack of an adequate 
information framework to support problem formulation. The APMs for this research area 
provide a conceptual framework for describing stressor-response relationships and an 
information framework for providing geospatial and toxicity data tailored to diagnostic 
applications (e.g., methods and models). Development of the geospatial database support system 
will be coordinated with the OW/OST because NHEERL will be adding to their BASINS 
modeling support system. These APMs will provide State, Regional, and Tribal authorities with 
critical and essential tools, which are currently unavailable, for starting the diagnosis process on 
an impairment problem. Ultimately, these information management tools can be incorporated 
into decision-support systems. 

Project Title 2. Classification Framework 

Project Coordination and Resources (10.6 FTEs: FY03: AED-0.4, GED-2.6, MED-5.0 [total = 
8.0]; FY04: GED-2.6 [total = 2.6]) 

Objectives 

Integrated hierarchical classification schemes will be developed at the scale of habitats, water 
bodies, watersheds, and regions to identify systems that are expected to respond similarly to 
aquatic stressors (see Sections 4-7). For example, estuaries with longer retention times are more 
susceptible to the effects of nutrient loading (Palter and Dettman 1999). The relative impact of 
suspended and bedded sediments via sedimentation and physical habitat alteration versus 
turbidity also will depend on retention time. Even the effect of toxic chemicals can be expected 
to vary systematically depending on physico-chemical characteristics of water bodies and 
sediments such as organic carbon, acid-volatile sulfides, suspended solids, and hardness 
(Hamelink et al. 1994, Bergman and Dorward-King 1997). 

Scientific Approach 

The central question that must be answered to determine if a classification system will be useful 
for diagnosis is, "does grouping of systems by class simplify the problem of determining the 
cause of the observed ecological effects which are equated with an impaired condition of a water 
body?" We propose to answer this question by developing classification systems that are keyed 
to the different levels of a nested spatial hierarchy that proceeds as follows: habitat, water body. 


148 


watershed, and region. Some classification schemes already exist for systems at each of the 
above levels of organization (Cowardin et al. 1979, Omemik 1987, McKee et al. 1992, Brinson 
1993, Maxwell et al. 1995, Frissell et al. 1986, Rosgen 1996, Detenbeck et al. 2000). To be 
useful in diagnoses, classification systems must be based on differences in the spectrum of 
forcing functions that result in differences in the behavior of systems among classes, (e.g., fluvial 
versus lagoonal geomorphology of a water body). The key to the viability of a classification 
system at any of these hierarchical levels is that the classes identified behave differently under 
the influence of the stressor of concern. Once classes have been identified based on existing or 
new classification systems, an initial screening of the stressor-response data at all four levels of 
organization will determine if research should proceed further on a specific stressor/class 
combination. 

Existing classification frameworks and necessary elements of an integrated classification strategy 
will be reviewed by a work group consisting of representatives from all Ecology Divisions, from 
each of the Aquatic Stressors research areas, and from experts on classification at each of the 
scales of interest. Representation will be requested from other ORD Laboratories and Centers, 
other Federal agencies, EPA Program Offices, and non-govemmental organizations as 
appropriate. Logical collaborators on this task include the Landscape Sciences Branch at NERL- 
Las Vegas, the Watershed Restoration planning group in NRMRL, OW [(OWOW), Office of 
Science and Technology (OST)], USGS (under NAWQA), FWS, NOAA, and the Nature 
Conservancy. Recent work on classification approaches within or outside of NFIEERL is 
summarized in Table 4. 

The classification workgroup will work towards the following goals: 

• Identification of key factors (forcing functions) controlling sensitivity of response to 
different classes of toxic and nontoxic (non-point source) stressors. 

• Identification of key factors determining sensitivity of response across multiple stressors 
to facilitate development of a comprehensive classification scheme rather than multiple 
schemes. 

• Development of national and regional classification frameworks. 

• Coordination of opportunities for testing classification strategies in a systematic fashion. 

Efforts of this workgroup will be supplemented by the extramural grants STAR program. The 
STAR grants program has an existing request for proposals (RFP) on aquatic ecosystem 
classification, and might add an RFP on watershed classification strategies in the future. 

Alternative strategies for classification will be tested through regional case studies (project 3). 
Regional case studies will be based on multiple-scale classification schemes, with coordination 
across Divisions to bring together appropriate areas of expertise. In particular, stressor-response 
relationships will be compared among regional/watershed/water-body classes. 


149 


W3 

C3 

O 

CO 

5 

eg 

"O 

c 

a 

"O 

o 

x> 

C8 

T3 

U 

CO 

u 

« 

« 


CQ 

C 

o 

‘ob 


03 

C 

o 

03 

u 

(z: 

CO 

CJ 

O 

CO 

D 

o 

03 

2 

Cu 

D. 

CQ 

■o 

CO 

O 

CL 

2 

CL 

u< 

O 

00 


CO 

'>< 

Ui 

Ji 

£) 

CQ 

H 


JH 


08 

3 

'M 

a 

4> 

fj 

G 

O 

u 


E 

o 

CJ 

k. 

J’-S > 

2 •= y 

•o 4) -o 

^•O C 
CO *2 

S 2 '5 

O CO 

? 

CO 

CO a> ^ 

J= £ 

03 ■£ J2 




03 

C 

o 

'Sb 

— ®0ct: 
^ '5J o o 
« tS o e 

s .i 

3 *0 O 2 

:£ "S! £ 2 

- O c 

<3 CO 60 w 


.2 

c 

4J 

a 


CJ 

&•§ 

i| 

8 l 

•i? ^ 

“ C/5 
>% CO 

sc CJ 


CO 

a> 

O I 

CO C 
k. 'S 

s = 

2 o 

? ‘S 
w c 

c « 
2 « 

!?•§ 
•O C3 


U 

E 

c 

c 

2 

« 

Oi 


3 

CJ 


3 ^ 

E o 

4> C 

J= —i 

<j r3 

o ^ 


<a 

o 

‘So 

_o 

c S- 

.3 60 03 

03 , CJ 

S « E 

V CJ Ir^ 

CJ .3 oj 


w ^ ^k 

3 ia ^ 

2 — ■« ■© "2 


c® ^ = ° 

H > 3 XI 


■o 

c 

3 

c 

.2 CO 
•- .2 
& E 

o <0 

k. "O 

E 

S 60 

s « 

CU 4J 


. e-^ 

•5 i“ 

.1 11 
60 OOcb 

2 o «= 

^ s 
C -o •— 


s 

X 

CJ 

3 

I” 

” I 

ltl 

S. O •§ 

•2 ^ 

-< ci. ft. 


k. 

o 

c 

£L 

ft 


IS 


k. 

11 

ft 

Ci. ft 


k. 

ft 

k. 

ft. 

ft 


T-l 

c « 

? 

« *= e 

« .2 ® 

1 « I 

2 o ^ 

I CO 

‘X "S 

"a c8 o 

S-2 ^ 


•2 

X 


4> 



C 

_o 

3 

Wm 

.a 
^ « 
4) 2 

'5 X 

3 CQ 

z ac 


w 

c 

4> 

■£ 

3 

z 


(3 

CJ 

E 

4J 

X 

CJ 

o 

’x 

o 

H 



« 1 2 

■3 c c S> 

.2 2 .2 5 E 

S 15 S 

o es 3 3 s 

E~< X c CO So 


2 

X 

cs 


i2 ® 

c .2 

■5 2 

3 4J 

3 w 

Z 13 


CO 

E 

<3 

V 

H 

C/5 


J 

« 

^ « 

2 j 

o 2 

u J 


-Si 

«5 

3 

c/3 

u 


</d 

i 


C/) 

a 


'i 

3 

C/) 

u 



I 


■S 

c/3 

2 

03 

:3i 

3 

c 

o 

‘So 

4J 

OC 


2 

X 

03 

a: 


X 

(3 

X 


■2 

X 


c 

o 

'eb 

u 

OH 


G 

«J 

jj 

X 


a 

0^ 


150 















CA 

u 

CA 

2 

es 

C 

CQ 

>» 

■g 

jD 

I 

« 

c 3 

•o 

U 

J= 

CA 

b> 

W 

cS 


cd 

c 

o 

’ 5 b 

cs 

c 

o 

*w 

cd 

u 

C 

*S 5 

CA 

— 

o 

o 

CA 

u 

JZ 

a 

D. 

O. 

ed 

"S 

CA 

O 

o. 

p 


bO 

c 

‘w 

CA 

’>< 

•^* 

X) 

cd 

H 


iS 

S 

X 

T 3 

3 


4> 

Ci> 

c 

o 

U 


a _ 

C CQ 
11 
1 s 

V E 

.a 5 

0^f\ O 


>» 

e>o 

. O 
>% •£ 

y e- 


00 .= 5 o 
^ " 3 E 


o c 


.2 S' g 

CQ «. 2 i S 


QO 


C 

00 

o 


E 

« 

eS 

CU 


c 

a 

C/) 

C 

<Q 


(A 

o 

C 

-2 § 

.3 cj 
3 O 
oo-s 
® *5 e 

^lll 

-i^l 

^ -C c »< 

CU Q. O V 




•5) g. 

O CQ 
2 ^ 

©.He 

•a 0-2 

I s I 

PQ oo 6. 


I 

3 

I- 

s l| 

til 

< >2i. ft. 


O 

■g 

E 

© 

2 

'i 

> 

*w 

3 

S 


c 

o 


•SIS 


CA 
« 
X) 

« .2 

•r* 

« 


> .iS 


o .t: 


3 3 

II 

o 03 


c 

c o 

o .. •© 

T-l — c/5 3 

^ 2 ig .2 


3 3 h: 


I II 8 „ 

« C = 5 ^ 3 
3 .2 o ® ^ X, 


S u 'Si ^ 


C «3 

X CA 

11 

Ja “ 



3 ; CA — Xi 4) 

3 «j E c C 3 O 2 

S o '3 ~ ^ 00 


W 

o 

C 

Ci. 


i 

e 


^2 

c 

© 

■5 

3 

Z 


CA 

4) 

'C 

3 

3 

CA 

u 


CA 

© 

— 

CA 4 ) 

E 'C 

A ea 

S 2 

CO <S 


3 

c 

0 

‘00 

4> 

0^ 


■s 

4> 

3 


3 

C 

o 

'00 

© 

0^ 


« 


ea 


e 3 _ 

S ^ .2 ^ 

" I w •£ « 

2 2 « .ts 

“•2 « -S 

4) 3 > 2 

£C CA > J= 


c 

o 

U 


e 

4 > 


a 

iS 

as 


CA 

<0 

00 

V 

> 

CA 


Q 

U 

O 


< 

a 

< 

z 

I 

CO 

O 

CO 

D 


o 

o 


4> 

CO 

CA 

V 

I 

CO 

D 


151 














CA 

JL) 

CO 

u 

V3 

a 


JS 

"eS 

3 

a 

4> 

W 

e 

o 

U 


>s W 

■O N w 

5 'S c 

i5 

" -Si >' E 

U 00 J= 

« ® ^ ii 

^ £ 8 S 

C ”0 60 . 

O >^ •■ e S 

■S I I 

« N W 2 ^ 

CQ £ 60 O 


^ o 

^ :5 

= § 

. C3 O 

c/3 "S 

■c 

S-5 S. 

« « J3/i 
8^0 
I i ^ 

■£= O 

U 


II 

s S 


u 

c 


8 

•T3 

_a 

"o 

_c 

o 

c/3 


o 

c: 


c 
c 
o 
o 
c 

3 
(U ^ 


3 

CM 

V 

« 


CM 

k« 

£ 

3 

73 

s 


CM 

V 

Jic! 

CQ 


60 

3 

O 


CM 

4> 

U 

b. 

3 

O 

<o ^ 
y 3 


Cb- 

O 


3 

O 


■a 
c 

C3 
. CM 
CM ID 
O C 
4> *3 .3 

CM 

V 

o 

c 


3 "c 

c fe O 5 

.E E ». 2 JO 


< ^ ja S 


£ Z « E 2 ‘22 cS 


u 

= f 

ca u 


3 u 


3 

CM c3 

£ ^ 


1 

o 

a 

I- 

- I- 

8 t‘l 

••• ^ 

■5 . r 3 

2 6©^ 
= ^ o 
< >2t ft. 


ft 

c 

ft. 


CQ 

c 

o 

03 

(J 

iS 

’S 

CM 

03 

O 

o 

w 

CM 

D 

J= 

2 

CL 

Cl 

03 

•o 

a> 

C/d 

g. 


ftO 

_c 

c/d 

*>< 

.3 

x> 

03 

H 




CM 


s 

4/ 

3 

Z 

1 

I 

S 

OS 


CO 

- c 

eo <; 


■o 

c 


V 


>% 

u 


mm — 

3 3 


g-O 

o 


u. £ 

4) '3 2 


(/i 


V C 


3 60 
*. w O' £ 
H U < (X 


152 










Products 


APM IB (GPRA # 202) FY03 Classification frameworks for geographic regions, watersheds, 
water-body, and habitat scales (MED, GED). 

Benefit of Products 

A classification framework will provide regional. State, and Tribal regulatory authorities a tool to 
collapse the over 40,000 water bodies requiring TMDLs into a more manageable number of 
similar units or water body classes. With defined water body classes, a TMDL template for 
remediating the impairment could be created which could then be applied to all of the water 
bodies within the class with minor adjustments. This would eliminate the need for 40,000 
unique TMDLs. 

Classification frameworks will also help to regionalize criteria development or definition of 
thresholds for impairment. This would improve the applicability of criteria to specific sites or 
classes of sites and lower the error rate in identifying impaired or threatened aquatic ecosystems. 
In particular, a watershed classification scheme within a regional framework will help to 
integrate and coordinate the 303(d) listing process at the watershed scale. 

Project Title i. Diagnostic Tool Development and Application through Regional Case Studies 

Project Coordination and Resources (36.55 FTEs: FY03: AED-3.25, GED-1.5, MED-8.4 [total = 
13.15]; FY04: AED-7.8, GED-2.5, MED-13.1 [total = 23.4]) 

Objectives 

• Develop diagnostic tools for single and multiple stressors; 

• Develop forecasting models; 

• Illustrate the application of diagnostic methods, tools and models for single and multiple 
stressors, including forecasting models; 

• Provide input to regional decision-support systems; 

• Demonstrate how assessment results can be extrapolated across regions, watersheds, and 
water bodies, and biological levels of organization; and 

• Illustrate how stressor-response relationships vary among different classes of systems in a 
predictable fashion. 


153 


Scientific Approach 

Case studies are a useful vehicle for developing and testing conceptual models, classification 
systems, diagnostic tools and models, and stressor-response relationships. Furthermore, case 
studies focused on specific places or issues of interest to the Agency provide an excellent 
mechanism to address high priority environmental problems, including the development of 
TMDLs. Diagnostic case studies will provide a mechanism for developing, testing, and applying 
methods and models for distinguishing among single aquatic stressors and allocating cause 
among multiple stressors. Case studies will be performed to incorporate the habitat, ecosystem, 
watershed, and regional spatial scales as well as the organismal, population, community, and 
ecosystem levels of biological organization. 

Case studies will be selected based on the following critical attributes: 

• Sites will be selected from those already designated as impaired (or threatened) based on 
the 305(b) or 303(d) reporting process, representing a range of degrees of impairment, a 
range of stressor combinations, multiple stressors with interaction potential, and common 
stressor-resource class combinations. 

• Sites will be selected to represent specific region-watershed-water body classes, such that 
results can be extrapolated using a regional or nationwide classification system. 

• Coastal systems will be selected, to include both watershed(s) and receiving waters. 

• Methods and information management will be coordinated across case studies. 

Additional desired attributes of regional case studies include the following: 

• Data have already been collected and/or analyzed in time series or at multiple time points. 

• Studies will involve cross-Agency collaboration. 

• Sites will have well-organized stakeholder groups [e.g.. National Estuarine Reserve 
Program, National Estuary Program, Areas of Concern or Lakewide Management Plan 
(LaMP) committees for Great Lakes]. 

• Studies will not duplicate assessments of well-studied systems. 

• Representative models exist. 

• Research or monitoring at the sites is ongoing. 

• Sites are in logistical proximity to an Ecology Division. 

Each case study will include the following elements: 


154 



• Study sites will be selected along stressor gradients within each class in the classification 
framework, to determine if stressor-response relationships vary among these classes. 

• Stressor gradients will be defined by land-cover/land-use attributes in order to provide the 
opportunity to develop simple diagnostic indicators of impairment at the watershed scale. 

• Gradients representing multiple stressors will be used to test multivariate methods in 
order to allocate variation in biological community composition to different 
environmental gradients or stressors. 

• Model development will incorporate both single stressor-response relationships and 
multiple stressor interactions to enhance forecasting of potential future impacts or 
recovery based on future scenarios. 

7. Great Lakes Coastal Watersheds/Wetlands/Nearshore Zones. 

Magnitude of watershed loadings and the relative sensitivities of instream and coastal ecosystems 
will be predicted and assessed through the application of watershed and coastal wetland 
classification schemes in the Lake Michigan basin. A watershed classification scheme based on 
hydrologic thresholds of response to forest fragmentation and watershed storage already has been 
tested for small coastal watersheds surrounding the western arm of Lake Superior across two 
distinct hydrogeomorphic regions (Detenbeck et al. 2000). This classification scheme is being 
extended to other regions with different single land-use gradients, and to watersheds with mixed 
land-use gradients (Simon 1999, Cincotta 2000). Through collaboration on a West Virginia 
REMAP project, watershed classification schemes are being developed for a region with 
mountainous terrain, and consider land-use gradients related to agriculture, urban and residential 
development, and mining. Through collaboration on a Great Lakes coastal wetlands REMAP 
project, watershed classification strategies are being developed for watersheds spanning a range 
of sizes (1 to 450 ha) and a mixture of land-uses. In addition, MED scientists have assessed 
differences in nutrient dynamics among hydrogeomorphic types of coastal wetlands, based on 
expected differences in retention time and relative influence of riverine versus lacustrine inputs. 
This approach will be extended to evaluate impacts from suspended and bedded sediment 
loading. For toxic chemicals, methodologies will be developed for predicting the parameters and 
variables which affect the bioavailability of the chemicals for the different levels of the 
classification schemes. 

MED will continue to develop a series of empirical stressor-response relationships in the Great 
Lakes basin for stressors known to constrain community composition for specific combinations 
of taxa and aquatic resource classes: a) temperature, flow, and clean sediments for instream fish 
communities; b) suspended and bedded sediments and flow for macroinvertebrate communities; 
c) flow, suspended and bedded sediments and nutrients for periphyton communities; d) nutrients 
and suspended and bedded sediments for coastal wetland vegetation; and e) habitat alteration and 
food-web structure for coastal fish populations and communities (Detenbeck et al. 2000). 

Current protocols for development of indices of biotic integrity (IBIs) yield indicators of general 
condition of biological communities for State 305b assessments, but do not yet provide 


155 


information on causes of biological impairment for the State 303d listing process. Analysis of 
existing State, REMAP, and EMAP monitoring databases ('www .epa.eov/einap/) and aquatic 
toxicity databases (AQUIRE, PHYTOTOX; www .epa.eov/ecQtox/ ) with associated exposure 
data will be coordinated across Divisions to derive diagnostic indicators to predict cause of 
impairment based on aquatic community composition. Examples of multivariate tools that can 
be applied to suggest causal hypotheses include nonmetric dimensional scaling (NMDS) 
ordinations to identify environmental gradients associated with gradients in community 
composition (Beals 1984), indicator analysis (Dufrene and Legendre 1997), discriminant function 
analysis relating environmental factors with species presence/absence (Scheller et al. 1998), and 
redundancy analysis to partition variance among multiple potential causal factors (Richards et al. 
1993). 

To address water column toxicity, EPA has developed TIE methods (Mount and Anderson- 
Camahan 1988, 1989; Burkhard and Ankley 1989; Mount 1989; Ankley et al. 1991; Norberg- 
King et al. 1991,1992; Durban et al. 1993; Mount and Norberg-King 1993; Burgess et al. 1996; 
Ho et al. 2002), which are a battery of physical/chemical manipulations coupled with toxicity 
tests. By determining which physical/chemical manipulation affect toxicity of the samples, the 
general characteristics of the causative chemical(s) can be determined. With this knowledge, 
appropriate analysis techniques and in some cases, in combination with additional sample 
fractionation techniques, are used to obtain a list of the tentative chemical(s) in the sample. With 
this information, toxicity tests using the suspected chemical(s) would be performed to establish 
the effect level for these chemicals in the water samples of interest and in reference waters using 
the TIE organisms. Successful TIEs occur when the concentrations of the suspected chemicals at 
the affect endpoint agree among the water samples and reference waters. 

For toxicity in sediments, substantial progress has been made to date for a number chemical 
classes and manipulations for whole sediments and sediment pore waters (Ankley and 
Schubauer-Berigan 1995, Besser et al. 1998, Ho et al. 1999, Leonard et al. 1999, Burgess et al. 
2000). With the successful development of solid-phase sediment TIE methods, field validation 
of interstitial water and whole sediment TIE methods is needed After development of the whole 
sediment and interstitial waters TIE methods, field validation of the methodologies are required 
to determine if the causes of toxicity identified by TIE represent the source of toxicity at the field 
site. Field validation will involve the TIE analysis of sediments with impaired benthic 
communities from both fresh and marine sites, and ideally, the causes of impairment for these 
sediments would not be some other stressor (e.g., suspended and bedded sediment or degraded 
habitat). Once a suspected toxicant is identified, field sediments and organisms would be 
analyzed. The final step in the validation process would be to reproduce the same community 
signature observed in the field, within laboratory-controlled situations by introducing the 
suspected toxicant into clean sediments in a mesocosm. The field validation effort will also 
allow the evaluation of benthic community signatures and toxicant relationships. If useful 
relationships can be developed, a library of chemical stressor-benthic community responses 
would be developed to complement relationships derived fi-om toxicity databases above, and this 
library would be developed on a water body class scale. Field validation will also permit the 
evaluation of toxicant/stressor and biological indices relationships for benthic communities. 
Specifically, a collaborative effort between MED and AED will seek to link cause and effect 
relationships observed in the laboratory to field effects using micro/mesocosm simulations. 


156 




A suite of ecosystem response models is being developed for Lake Michigan that link inputs 
from tributanes to their associated large receiving water bodies and ecological responses. The 
construct is best described as mechanistic, mass balance models, and the primary suite of coupled 
and linked models being applied include: atmospheric, meteorological, hydrodynamic, sediment 
transport, eutrophication, sorbent dynamics, water quality transport and fate, and food chain 
bioaccumulation. The modeling focuses on establishing relationships of contaminant and 
nutrient loadings and ambient concentrations with chlorophyll, DO, N/P ratios, phytoplankton 
species composition, lower food chain produaivity, water column transparency, habitat, fish 
consumption advisories, and lake trout egg hatchability. These models improve the predictive 
ability for forecasting environmental benefits of specific load reduction scenarios of nutrients and 
contaminants, as well as the time to realize those benefits. 

2. Shallow Estuarine ^sterns in the Northeast Atlantic. 

A case study is proposed by AED to quantify the endpoint parameters being proposed in the 
Habitat Alteration, Nutrients, and Toxic Chemicals implementation plans (see Sections 4, 5, and 
7, respectively), integrate the results within the conceptual framework proposed in this Section, 
and use this information to test the utility of stressor-response relationships and diagnostic 
methods and models under development The initial research studies will be carried out within 
the coastal ecosystems of the Northeastern U.S., particularly the Narragansett Bay and 
neighboring coastal systems in Regions 1 and 2 at sites listed for TMDL development. 

Two stressors will be emphasized initially: nutrients and toxic chemicals. Several related 
projects will examine the effects of a range of nutrient loadings on several different coastal 
ecosystems, (e.g., marshes, shallow coves, and small estuaries) through field studies and model 
development. Concurrently, another project will study the effects of several classes of toxic 
chemicals on organisms, ix)pulations, and communities that dwell in critical habitats along a 
salinity gradient from fresh to salt water. Each investigation will synthesize data in a manner that 
allows us to characterize the contribution of each stressor to adverse ecological effects. For 
example, a collaborative effort between MED and AED will seek to link cause and effect 
relationships observed in laboratory to field effects using micro/mesocosm simulations. These 
studies will be integrated through an ecological model that examines the individual and 
interactive effects of nutrients and specific toxic chemicals on important habitats. This model 
will be validated by laboratory and field studies of systems where both nutrients and toxic 
chemicals are thought to be responsible for observed conditions. 

3. Shallow Estuarine Systems in the Gulf of Mexico. 

A case study is proposed by GED to quantify the endpoint parameters being proposed in Sections 

4. 5, and 7, respectively, integrate the results within the conceptual framework proposed in this 
Section, and use this information to test the utility of stressor-response relationships and 
diagnostic methods and models under development. The initial research studies will be carried 
out within the coastal ecosystems of the Gulf of Mexico, particularly the Pensacola Bay and 
neighboring coastal systems in Regions 4 and 6. 


157 


Again two stressors will be emphasized initially: nutrients and toxic chemicals. The first step in 
the approach will be to examine existing 303(d) impairment lists, databases on nutrients and 
toxic chemicals, and land use/land cover characteristics for Gulf of Mexico estuaries to delineate 
four classes of sites based on observed effects or criteria. The four classes include sites affected 
by nutrients only, toxic chemicals only, both nutrients and toxic chemicals, and neither nutrients 
nor toxic chemicals. A candidate suite of biological indicators would then be developed that 
demonstrates differential sensitivity to either stressors. This would require examination of 
historical effects databases and population or community response data as well as receiving input 
on single-stressor response models for nutrients and toxic chemical efforts (see Sections 5 and 7). 
In collaboration with AED, lab or field tests will be used to validate the sensitivity of these 
indicators in each of the four classes of sites. If historical data exists for indicators in the study 
areas, tests will confirm the sensitivity of indicators to these two stressors. Multivariate analysis 
methods will be applied to allocate variation in the response indicators to differentiate between 
nutrient and toxic effects. Modeling approaches will then be used to integrate individual and 
interactive effects of nutrients and toxic chemicals on biological indicators. Models would 
account for population and community levels of response across the four classes of sites and 
along stressor gradients. GED and AED will coordinate the development of models within the 
context of the classification framework and diagnostics. 

4. Coordination with other Goal 2 Research (WED). 

The freshwater habitat alteration group at WED is developing a project to examine the influence 
of human activities on native fish habitat at reach, watershed, and landscape scales. This group, 
led by Jim Wigington, is developing salmon and native fish assemblage modeling approaches 
while concurrently evaluating the interactive influences of flow, temperature, physical habitat, 
and nutrients on salmon and native fish. This project is focused on coastal drainages of Oregon 
where there is a great opportunity to contribute to the restoration of salmon populations through 
cooperative research efforts with State (Oregon Department offish and Wildlife, Department of 
Environmental Quality) and other Federal agencies (NMFS, U.S. Forest Service). 

5. Coordination with GPRA Goal 8 (AED, GED, MED, WED). 

Goal 2 activities under Diagnostics will be coordinated with Goal 8 activities in monitoring and 
assessment through the EMAP program. There are currently monitoring initiatives underway 
through: 1) the Coastal Initiative, examining the condition of marine estuaries; 2) the STAR 
grants program to develop indicators for coastal freshwater and marine systems, (including the 
GLEI cooperative agreement for indicator development on coastal Great Lakes systems); 3) 
Western EMAP (including coastal watersheds in the states of Washington, Oregon, and 
California as intensive monitoring sites); and 4) a variety of Regional EMAP projects. The latter 
program currently emphasizes watershed-scale approaches to monitoring and assessment. For 
example, a recently initiated REMAP project with the state of West Virginia will demonstrate 
both the development of a watershed classification system and test thresholds of land-use/land- 
cover along gradients of disturbance (related to forestry, agricultural, development, and mining 
activity), while at the same time developing fish indices of biotic integrity. 


158 


6. Coordination with other State, Regional, and Federal agencies (AED, GED, MED, WED). 

Development of regional case studies will take advantage of ongoing monitoring and assessment 
activities by State, regional, and Federal agencies. The USGS NAWQA program is examining a 
nationwide approach to regionalization of watersheds, based on common vulnerabilities to 
nonpoint stressors (McMahon and Cuffney 2000 and Carolyn Couch, personal communication). 

Within this regional classification 
framework, watersheds in NAWQA 
study units are being selected along 
gradients of urbanization to examine 
response of stream ecosystems to 
development pressures. Each of the 
NHEERL divisions is located in 
proximity to NAWQA study units that 
are scheduled to adopt this approach 
during the near future, with the start of 
the second 10-year phase of monitoring 
(units in dark blue on Figure 15). 


In addition, opportunities exist to 
coordinate studies with the National 
Estuarine Program (NEP), Great Lakes 
Areas of Concern, and Great Lakes 
LaMPs. 

Figure 15. Locations of national water-quality 
assessment study units. 

Products 

1. Single stressor-methods and models. 

APM 2A FY03 Guidance document on whole sediment TIEs ( MED, AED). 

APM 2C FY05 Guidance on and user-friendly interface for derivation of diagnostic indicators for 
individual stressors (MED, AED). 

2. Multiple stressor-methods and models. 

APM 3 A FY02 Case studies of multivariate approaches to community data analysis to apportion 
cause among stressors (AED, MED). 

APM 3B FY06 Methods and models for multiple stressors with case studies (MED, AED). 


LOCATIONS OF NATIONAL WATER-QUALITY 
ASSESSMENT STUDY UNITS 



h i Began in fiscal year 1991 
I I Began in fiscal year 1994 
n Began in fiscal year 1997 
I I Began in fiscal y^r 1999 
High Plains Reg. Ground Water Study Not scheduled 


159 














Forecasting Approaches 

FY07 Methods and models to support alternative remediation schemes to achieve specific 
management goals (MED). 

3. Case Studies. 

APM 2B FY05 Application of coastal watershed and estuarine/lacustuary classification schemes 
to predict probability of impairment based on Great Lakes and Gulf of Mexico regional case 
studies (GED, MED). 

Benefit of Products 

Diagnostic tool development will produce the single stressor, multiple stressor, and forecasting 
methods and models necessary to determine the causes of adverse effects on intact water bodies. 
Regional case studies will provide the basis for verifying the efficacy of these diagnostic tools. 
Further, regional case studies will provide the basis for development of the guidance listed 
above, and allow diagnostic tools to be demonstrated to stakeholders in sites where TMDLs need 
to be developed. These studies will enable OW to understand how multiple stressors, such as 
nutrients and toxic chemical loadings, affect important habitats separately and in combination for 
several types of coastal ecosystems across the U.S. We expect the methods and models 
developed here to be generic for specific stressor-ecosystem combinations. Therefore, we predict 
that they can be applied in other regions that contain similar stressor-ecosystem combinations. 
Classification schemes will allow us to regionalize results and recommendations for TMDLs and 
watershed restoration activities. The scientific approach used here is also generic and it could be 
applied to develop similar relationships for the ecosystems and stressors that predominate in any 
region. 

Project Title 4, Generic Models for the Evaluation of Multiple Stressor Interactions 

Project Coordination and Resources (1.5-8.0 FTEs: AED, GED, MED, starting in FY04/05, 
increase over time) 

Objectives 

The objective of this research area is to assess the likelihood that synergistic and/or antagonistic 
ecological effects will occur from the interactions of multiple stressors. To set priorities for the 
development of TMDLs and the restoration of impaired water bodies, it is necessary to 
understand how the potential interactions among stressors will affect system recovery once one 
of the stressors is reduced. For example, in a turbid coastal wetland, a reduction in suspended 
solids loading without an accompanying reduction in P loading from upstream animal feed lots 
could unmask a eutrophication problem that was previously not evident due to light limitations 
on primary production. 


160 


Scientific Approach 

As a first step, key combinations of stressors for which interactions are expected to occur will be 
identified based on mechanisms of action outlined in conceptual models and review of 303(d) 
listings for common combinations of stressors. Individual stressor dose^•esponse relationships 
and models developed under project 3 will provide a starting point for examining the interactions 
of multiple stressors in freshwater and marine ecosystems. The importance of interactive effects 
will be evaluated by including the documented pathways of stressor action and interaction in 
deterministic dynamic models calibrated with field studies and/or historic data, and then 
simulated over many runs to discover the sensitivity of measurement endpoints to changes in one 
or more of the stressors (e.g., Bartell et al. 1984, Mitsch and Reeder 1991, Hanratty and Stay 
1994, EPA 2000c). A generic model including the impact and interaction paths for the dominant 
stressors of interest will be applied for each of three resource classes: streams, lakes and 
reservoirs, and estuaries. These generic model frameworks will be developed as a joint product 
among the four Ecology Divisions. Sensitivity analysis of these models will serve as a first order 
estimator for allocating observed ecological effects among two or more interacting stressors, as 
well as a means for evaluating the relative importance of indirect and interactive effects. This 
approach is independent of scale requiring only that the stressor- response relationships and 
interaction pathways be documented on the scale of interest. Once expected interactive effects 
and ranges of interactions are identified, the results of existing case studies and ongoing regional 
case studies will be reviewed for evidence of interaction effects. Pending outcomes of simulation 
exercises, additional field studies will be performed, combined with carefully crafted laboratory 
experiments and physical models calibrated to match loadings and functional properties observed 
in the field system. 

Products 

FY03 Identification of key combinations of stressors expected to interact within conceptual 
model (all Ecology Divisions). 

APM 3B FY06 Simulation of key stressor interactions with generic ecosystem models using 
sensitivity analysis to define the range of stressors and stressor combinations under which 
nonadditive interactive effects will occur (MED, AED). 

Benefit of Products 

Part of the TMDL process involves allocation of the cause of impairment among multiple 
stressors. The simplest case possible involves additive effects, which can be predicted from 
single stressor models. If effects are synergistic or antagonistic, then the results of reduced 
loadings will be more difficult to predict. The proposed products would identify the extent to 
which stressor interactions are expected to occur in natural ecosystems and those combinations of 
factors which favor their occurrence. This knowledge is critical to allow the States and Tribes to 
develop viable restoration and remediation plans for water bodies and watersheds affected by 
multiple stressors. 


161 


Project Title 5, Decision^Support System 

Project Coordination and Resources (3.0-6.0 FTEs: AED, GED, MED, starting in FY05/06, plus 
programming support) 

Objectives 

The objective of this research is to develop a decision-support system for diagnosing the causes 
of biological impairment at multiple scales. The decision-support system will be based on 
conceptual models outlining expected cause-effect relationships involving single and multiple 
stressors (project 1). At the most basic level, this interface will consist of a guide to the use of 
existing EPA databases, methods, and models. As tools are developed for diagnosis of single or 
multiple stressors, these will be incorporated into an expert-system frameworic. Simple empirical 
relationships between exposure and response indicators will be incorporated, as well as more 
detailed mechanistically-based models. 

Scientific Approach 

Phase 1. Identify existing tools, methods, and models available to support establishment of 
cause-effect relationships 

A conceptual model is proposed to provide a framework for documentation of existing tools to 
support diagnosis (Figure 16). Within this conceptual framework, it is possible to develop 
methods and models that bypass many of the linkages, for example, relating alterations in land- 
use/land-cover within an existing watershed class to projected effects on fish community 
integrity. Examples of existing methods and models available to establish or confirm the cause- 
effect linkages in Figure 16 are listed in Table 5. 

Phase 2. Produce a decision support system design 

Existing decision support systems will be investigated to choose an appropriate approach. Based 
on the apfx*oach chosen, a system framework will be developed and a document produced to 
instruct collaborators on the intent and use of the decision-support system and to build 
management support for this system through practical demonstrations of its utility. 

The technology now exists to produce a computerized, web-based decision support system. The 
BASINS system supported by OW consolidates a geospatial framework to support a 
comprehensive diagnostic decision-support system. At present, it is a collection of models and 
databases organized around a GIS-based computer program. This system will be expanded to 
include the information and tools produced under this Aquatic Stressors research effort. ''Exp)ert 
system" modules will be added that would lead persons in the Program Offices and State 
agencies through a guided, rule-based scenario that leads to a diagnosis of the causes of 


162 






I 

I 

i 

f 

p 

\ 


( 

» 

{ 

i 

t 




t 


» 


> 

I 


t 



I ’ 


“j 

o 



c 



o 


o 

o 


c 

C 


s 

! E 



! 3 


c 



0) 

8 


E : 

w 


T3 

0) 


0) 

1 « 


CO 1 

§ 


■ 


S' 


• • XT 

1 - 


■ 



e 

O 


o 

© 





C 

C3I 


<0 ' 



Q. : 


v> 



0) 



’.rs 


</> 

C 


0) 

3 



E 

E 


c 

3 

E 

8 


E 

c 


s 

o 



£ 


o 

c 


£ 

<0 


c 

Q. 


0) 

o 


OQ 

o 



N 



---.A 





li'ilil: 




■ :l: ! ! 





...pi 








163 


Figure 16. Conceptual model of cause-and-effect relationships in coastal systems, providing a framework for a decision support 
system. See key to model components at base of figure. Loading terms include atmospheric component. 









































































































































































































164 

















impairment, the development of a TMDL, and recommendations for restoration of remediation 
(e.g., see Reynolds 1999a,b; http://www.fsl.orst.edu/emds/ ). 

Phase 3 . Development and implementation 

A team will be formed consisting of research scientists, modelers, computer systems analysts, 
and WEB page designers to develop and implement the system. Contact persons from each of 
the research teams working in the Aquatic Stressors research program will be assigned to provide 
liaison with the decision support team. If approfM’iate, other team members and contacts would 
be provided from other EPA Laboratories, Program Offices, and State agencies. This 
development effort would produce the decision support system and a guidance document on its 
application. 

Phase 4 . Application 

During the final stages of development, data, models, and information from selected case studies 
will be incorporated into the expert system. This would be done in cooperation with the decision 
personnel associated with the studies and training would be provided along with a guidance 
document and system documentation. 

Products 

FY04 Framework(s) for decision-support system (all Ecology Divisions). 

FY06 Decision-tree guiding application of approaches with case studies (all Ecology Divisions). 

APM 3C FY07 Decision-support system(s), including forecasting of future cause-effect 
relationships (MED). 

Benefit of Products 

A series of decision-support systems will provide States and Regions with a common set of tools 
for approaching TMDL development and assessing alternative options for watershed restoration. 
Existing tools and databases will be made readily available, and gaps in knowledge bases will be 
more readily identified. Decision-support systems will also facilitate an integrated approach to 
assessment and diagnosis at the regional scale, thus improving the efficiency of the TMDL 
process. 

Gap Analysis 

Gaps by geographic region, biological scale, resource class, and stressor type 

The research tasks outlined above will be applied within coastal watersheds bordering on both 
freshwater and saltwater receiving water bodies. Regional case studies will be developed within 
each of three areas, the Great Lakes, the Atlantic coast, and the Gulf coast. This research area 
will depend heavily upon each of the other research areas of this document (Sections 4-7) for 


165 



development of stressor-response relationships for individual stressors and will coordinate with 
these respective research groups in the development of diagnostic methods and models for 
individual stressors. The focus of each stressor-specific research area is outlined below in Table 
6, with the biological endpoints and spatial scale of investigation noted Gaps have been 
identified relating to the investigation of nutrient effects in freshwater streams and rivers, and the 
investigation of clean sediment effects on coastal wetlands and estuaries. Because these are 
significant stressors in the EPA Regions, biological scales and resource types of interest, the 
diagnostics work will have to be supplemented by work in other research areas or rely on 


Table 6. Single aquatic stressors method development covered by other research areas witiiin 
the Aquatic Stressors Framework {in italics = endpoints: biological scale (organism, 
population, community, ecosystem); spatial scale (habitat, water body, watershed, region)] 


Resource class 

Stressor 

Fresh Water 
Coastal & Lakes 

Fresh Water 
Watershed 

Marine Coastal 

Nutrients 

Community, 

Ecosystem 

Nutrient Group 


Organismal (SAV) 
Population (SA V) 
Community, 

Ecosystem, 

Watershed 

Nutrient Group 

Suspended and 

Bedded Sediments 


Habitat, community 

Organismal (SAV) 
Population (SAV) 

Habitat Alteration 

Population scale. 
Habitat Alteration 
Group 

Salmon populations. 
Habitat Alteration 
Group 

Shrimp populations. 
Habitat Alteration 
Group 

Toxic Chemicals 

Population level. 

Toxic Chemicals 
Group 

Population level. 

Toxic Chemicals 
Group 

Organismal, 

population, 

community. 

Toxic Chemicals 
Group 


existing literature to fill these gaps. In addition, the lack of effects work at the community level 
for many resource class/stressor combinations will need to be addressed, as this is the biological 
scale at which the States are assessing biological impairment in current monitoring programs. 

Other gaps have been identified by geographic region. Expertise available at AED and GED will 
be supplemented with expertise in watershed classification and assessment through collaboration 
with other research groups (e.g., MED or WED, USGS NAWQA program). Development of 
regional case studies or decision-support systems for the Pacific Northwest will require close 


166 












collaboration with Goal 8 research efforts, and/or other research areas under the Aquatic Stressor 
research effort. 

Gaps by skill area and level of effort 

The level of effort and required areas of expertise for each of the diff^ent research tasks 
discussed in this document are summarized in Table 7. Overall, the total number of FTEs 
estimated for implementation of the Diagnostics plan is consistent with the total number of FTEs 
allocated to Diagnostics across all Ecology Divisions. This conclusion is based on two 
assumptions: 

1. There will be close coordination with other Aquatic Stressors research areas (Sections 4-7) in 
the development of conceptual models, classification frameworks, implementation of regional 
case studies, and development of decision support systems. 

2. The requisite expertise available within the Ecology Divisions will be made available for 
diagnostics research by management at the respective Divisions. 

Some gaps do exist in the types of expertise available relative to resource needs. Additional 
expertise and/or support will be needed for statistics (design issues), futures analysis 
(socioeconomics, ecological economics), near coastal modeling for marine systems, ecosystem 
simulation modeling, and programming support for decision-support system design and 
development. Some of these needs could be met through additional infrastructure support for 
existing contracts (statistical support available through AED, ADP support contracts at all 
Divisions)^ while other needs must be factored into long-term hiring plans (ecological 
economics, socioeconomics) or development of extramural grants. Some modeling support 
needs could be met through coordination with NERL Divisions, Regions, or Program Offices, 
but additional support will be needed to develop food web components of models. 


167 


Detenbeck, N.E., Batterman, S.L., Brady, V.J., Brazner, J.C., Snarski, V.M., Taylor, D.M., 
Thompson, J.A., Arthur, J.W. 2000. A test of watershed classification systems for ecological risk 
assessment. £/7v/ro«. Toxicol Chem. 19:1174-81. 

Dufrene, M., Legendre, P. 1997. Species assemblages and indicator species: the need for a 
flexible asymmetrical approach. EcoL Monogr. 67:345-366. 

Durban, E.J., Norberg-King, T., Burkhard, L.P. 1993. Methods for aquatic toxicity identification 
evaluations: phase II toxicity identification procedures for samples exhibiting acute and chronic 
toxicity. EPA/600/R-92/080. Environmental Research Laboratory, Duluth, MN. 

Engle, V.D., Summers, J.K. 1999. Refinement, validation, and application of a benthic condition 
index for Gulf of Mexico estuaries. Estuaries 22:624-635. 

Engle, V.D., Summers, J.K., Gaston, G.R. 1994. A benthic index of environmental condition of 
Gulf of Mexico estuaries. Estuaries 17:372-384. 

EPA. 1991. Guidance for water-quality-based decisions: the TMDL process. EPA 440-4-91-001. 
Office of Water, Washington, DC. 

EPA. 1996. Proposed guidelines for ecological risk assessment. EPA-630/R-95-002B. 
Washington, DC. 

EPA. 1998a. Clean Water Action Plan. EPA 840-R-98-001. 

EPA. 1998b. Better assessment science integrating point and nonpoint sources: BASINS version 
2.0. EPA-823-B-98-006. Office of Water, Washington, DC. 

EPA. 1999. Clean water action plan: restoring and protecting America’s waters. EPA-840-R-98- 
001. Washington, DC. 

EPA. 2000a. National water quality inventory: 1998 report to Congress. EPA-841-F-00-006. 
Office of Water, Washington, DC. 

EPA. 2000b. Aquatox release 1: a simulation model for aquatic ecosystems. EPA-823-F-00-015. 
Office of Water, Washington, DC. 

EPA. 2000c. Stressor identification guidance document. EPA-822-B-00-025. Office of Water 
and Office of Research and Development, Washington, DC. 

EPA. 2001. 1998 TMDL tracking system (version April 2, 2001). 

Federal Register. 2000. Unified federal policy for a watershed approach to federal land and 
resource management. 62566 Federal Register, Vol. 65, No. 202, Wednesday, October 18,2000. 


170 


Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D. 1986. A hierarchical framework for stream 
habitat classification: viewing streams in a watershed context. Environ. Manag. 10:199-214. 

Hamelink, J.L., Landmm, P.F., Bergman., H.L., Benson, W.H., eds. 1994. Bioavialability: 
Physical, Chemical and Biological Interactions. Lewis Publishers, Ann Arbor, MI. 

Hanratty, M.P., Stay, F.S. 1994. Field evaluation of the littoral ecosystem risk assessment 
model's prediction of the effects of chlorpyrifos. J. Appl. Ecol. 31:439-53. 

Ho, K.T., Kuhn, A., Pelletier, M.C., Burgess, R.M., Helmstetter, A. 1999. Use of Ulva lactuca to 
distinguish pH dependent toxicants in marine waters and sediments. Environ. Toxicol. Chem. 
18:207-212. 

Ho, K.T. et al. 2002. Methods for aquatic toxicity identification evaluations of freshwater and 
marine sediments. EPA, Office of Research and Development, Atlantic Ecology Division/Mid- 
Continent Ecology Division, Narragansett, RI/Duluth, MN. (in prep.) 

Hunter, R.S., Niemi, G.J., Pilli, A., Veith, G.D. 1990. Aquatic information and rEtrieval 
(AQUIRE) database system. In Pillman, W., ed., Envirotech Vienna 1990: Computer 
Applications for Environmental Impact Analysis. International Society for Environmental 
Protection, Vienna, Austria, pp. 42-48. 

Leonard, E.N., Mount, D.R., Ankley, G.T. 1999. Modification of metal partitioning by addition 
of synthetic A VS to freshwater sediments. Environ. Toxicol. Chem. 18:858-864. 

Lewis, M.A., Weber, D.E., Stanley, R.S. 2000. Wetland plant seedlings as indicators of near¬ 
coastal sediment quality: interspecific variation. Mar. Environ. Res. 50:535-540. 

Lewis, M.A., Weber, D.E., Stanley, R.S., Moore, J.C. 2001. Relevance of rooted vascular plants 
as indicators of estuarine sediment quality. Arch. Environ. Contam. Toxicol. 40:25-34. 

Lores, E.M., Murrell, M.C., DiDonato, G.T., Stanley, R.S., Snyder, R.A., Sipura, J., Flemer, 

DA. 2002a. Effects of zooplankton grazing on phytoplankton communities in Escambia Bay, FL. 
Mar. Ecol. Prog. Ser. 39 pp. (submitted.) 

Lores, E.M., Lewis, M.A., Malaeb, Z.A. 2002b. Spatial and temporal variability in zooplankton 
community dynamics in three urbanized bayous of the Pensacola Bay system, Florida, USA. Gulf 
Mex. Sci. 21 pp. (submitted.) 

Maxwell, J.R., Edwards, C.J., Jensen, M.E., Paustian, S.J., Parott, H., Hill, D.M. 1995. A 
hierarchical framework of aquatic ecological units in North America (Nearctic Zone). Technical 
report, USDA, Forest Service, NC. 


McKee, P.M., Batterson, T.R., Dahl, T.E, Glooschenko, V., Jaworski, V., Pearce, J.B., Raphael, 
C.N., Whillans, T.H., LaRoe, E.T. 1992. Great Lakes aquatic habitat classification based on 


171 


wetland classification systems. Chapter 4. In Dieter, W. Busch, N., Sly, P.G., eds.. The 
Development of an Aquatic Habitat Classification System for Lakes, CRC Press, Ann Arbor, MI. 

McMahon, G., Cuffney, T.F. 2000. Quantifying urban intensity in drainage basins for assessing 
stream ecological conditions. J. Am. Water Resourc. Assoc, (in press). 

Mitsch, W.J., Reeder, B.C. 1991. Modeling nutrient retention of a freshwater coastal wetland: 
estimating the roles of primary productivity, sedimentation, resuspension and hydrology. Ecol. 
Model. 54:15U\^7. 

Mount, D.I. 1989. Methods for aquatic toxicity identification evaluations: phase HI toxicity 
confirmation procedures. EPA/600/3-88/036. EPA, Office of Research and Development, 
Environmental Research Laboratory, Duluth, MN. 

Mount, D.I., Anderson-Camahan, L. 1988. Methods for aquatic toxicity identification 
evaluations: phase I toxicity characterization procedures. EPA/600/3-88/034. EPA, Office of 
Research and Development, Environmental Research Laboratory, Duluth, MN. 

Mount, D.I., Anderson-Camahan, L. 1989. Methods for aquatic toxicity identification 
evaluations: phase II toxicity identification procedures. EP A/600/3-88/035. EPA, Office of 
Research and Development, Environmental Research Laboratory, Duluth, MN. 

Mount, D.I., Norberg-King, T. 1993. Methods for aquatic toxicity identification evaluations: 
phase III toxicity confirmation procedures for samples exhibiting acute and chronic toxicity. 
EPA/600/R-92/081. Environmental Research Laboratory, Duluth, MN. 

Murrell, M.C., Stanley, R.S., Lores, E.M., DiDonato, G.T., Smith, L.M., Flemer, D.A. 2002. 
Evidence that phosphoms limits phytoplankton growth in a Gulf of Mexico estuary: Pensacola 
Bay, FL, USA. Mar. Ecol. Prog. Ser. (submitted). 

Norbeig-King, T., Mount, D., Durban, E., Ankley, G., Burkhard, L., Amato, J., Lukasewycz, M. 
Schubauer-Berigan, Mil., Anderson-Camahan, L. 1991. Methods for aquatic toxicity 
identification evaluations: phase I toxicity characterization procedures, 2^ ed. EP A/600/6- 
91/003. Environmental Research Laboratory, Duluth, MN. 

Norberg-King, T.J., Mount, D.I., Amato, J.R., Jensen, D.A., Thompson, J.A. 1992. Toxicity 
identification evaluation: characterization of chronically toxic effluents, phase I. EP A/600/6- 
91/005F. Environmental Research Laboratory, Duluth, MN. 

Omemik, J.M. 1987. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geogr. 
77:118-125. 

Palter, J.B., Dettmann, E.H. 1999. The effects of nitrogen loading and freshwater residence time 
on the estuarine ecosystem. 15*** Biennial International Conference of the Estuarine Research 
Federation, September 25-30, 1999, New Orleans. 


172 


Reynolds, K.M. 1999a. EMDS users guide (version 2.0): knowledge-based decision support for 
ecological assessment. Gen. Tech. Rep. PNW-GTR-470. U.S. Department of Agriculture, Forest 
Service, Pacific Northwest Research Station, Portland, OR. 63 pp. 

Reynolds, K.M. 1999b. NetWeaver for EMDS version 1.0 user guide: a knowledge base 
development system. Gen. Tech. Rep. PNW-GTR-471. U.S. Department of Agriculture, Forest 
Service, Pacific Northwest Research Station, Portland, OR. 75 pp. 

Richards, C., Host, G.E., Arthur, J.W. 1993. Identification of predominant environmental factors 
structuring stream macro invertebrate communities within a large agricultural catchment. 

Freshw. Biol. 29:285-294. 

Rosgen, D.L. 1996. Applied river morphology. Wildland Hydrology, Pargosa Springs, CO. 

Scheller, R.M., Snarski, V.M., Eaton, J.G., Oehlert, G. 1998. An analysis of the influence of 
annual thermal variables on the occurrence of warm water fishes. Trans. Am. Fish. Soc. 128:257- 
264. 

Simon, T.F. 1999. Regional environmental monitoring and assessment (REMAP): evaluation of 
the Great Lakes nearshore coastal wetlands: with emphasis on development of watershed 
environmental indicators and status. R-EMAP proposal submitted to EPA, Mid-Continent 
Ecology Division, Duluth, MN. 


173 


Appendix 1. Critical path table identifying implementation stages and tasks for States from monitoring through diagnosis, associated uncertainties, 
research needs, and research products (blue color refers to products being produced in whole or in part by other research areas). 


cS 




oa 

c 


c 

o 

p 


c o 


(U D 

c .2 

o ^ 

Cu ^ 
t/5 O 


c 

o 

<D 

U 

C 


-o 

c 

a 


Cl 

Cl 

c3 

c/o 


r3 D 

O -w 


o 


c 

— <N 

O 00 

a, 

"a 15 

^ c o 

O <U c/5 


c 

00 

•tS G 
w O 
00 .G 

^ .ts 

o T3 

r- C 

n ® 
O o 

1) D 
O o 

c C 

•-H « 

3 oG 

O 2a 


00 


3 

CJ 


c 
o 
o 

CJ 

c 

C3 

T3 

•5 -2 
O •= 

. . CJ 

U 

(N g 

S S) 

Cl ra 

< -o 


oo 

O 

O 

-il oo 

o <u 

CJ T3 

3 


(U 

C 


CJ 

3 


O 

■a w 
3 ca 
aj _> 
'vC 

0-- flj 

iL "O 
O i- 

3 0^- 


T3 

<U 

JC 

c/5 

0. 

<U 

C3 


•3 

O (N 

L. ^' 

D- ^ 

■S 2 
£.3 

-O 

E .E 



« 3 

00 ^_, 

<D ^ 

T3 OJ 

6X) ^ 

.S ^ 

>—1 " 0(—I 

a UJ 

E 


</5 

O 

c 

W) 

E •- 

3 


4- (D 

H 3 I 

O c: > 

O (U x: 

J- 00 
4- ^ (D 

^ (D 0*— 


Cl c/5 

M) 


00 

Li 

O 


^ 3 

c3 3 _ 

00 QN r; 

"C 

O ^ ® "3 

; eg w cd 

sa -'s ^ 

^ • vpH 

(D 0) O "3 ^ 

Q ‘sd g .S 3 


cu 


C5X) •- 
c ^ c/5 

i:E § 

E :e 


ea 

oo 

O 


3 

lU 

_D 

(U 

3 

’ L 

3 

E 

'a5 


3 

> 

3 

Q 


5 1 

3 O 
■3 3 


3 

3 

00 

c/5 

3 

3 

‘5o 

_o 

o 

3 

3 

i_ 

3 


c/5 -I-- 

3 O 


L 

o 

■>-> 

3 

3 


3 


3 05 

3 -G 3 >o 
-—3 — o 
3 3 3 — 

3 c/5 3 3 

c/5 00 3 

^ _ c/5 

C .ti C 




c/5 

3 


3 

x: 

3 

3 

S 

::3 


3 

l-l 

3 

> 


00 

3 


rJ 2a CS 
*3 ^ ^ 

TS 5 3 
Gi) O -O 
C CL •' 
X - 
3 oo 

3 

■E X 

.2 o 


•C 

o 


L 

O 

13 


3 

Cu 

3 C' 
C w 
3 3 

.3 -r 


3 

•1.^ 

2a 

CL 

E 

o 

3 

C 


2a 

13 

3 

00 


c 

o 

E 


c 

'o 


5- c: 
o O 

L C 

CL o 
3 — 


3 

w 

•C 


o- § 
^ E 

3 

L 


L 

CL 

CL 

3 


3 3 

l_ jr; 

u. 

O 


U- ^ 

-a 


O ^ 

3 


X) 

CL 


o g 

_o 


:e -2 

3 

> 

cd 

’.E 

3 

*21 

E E 

-o 

C 

0) 

•4—) 

Q 8 


’u 

O 



c/5 

3 

JS 


CO 


00 CO ^ 

O 3 
C O 
CUD LC 


CO 

3 3 

2 It; 

c 


Q E 




.2 

’c/3 

• ^ 
CJ 

o; 

Q 


.-L 00 

bfl *-• 

• C 

3 3 

I > 

3 


CO 

CL 

3 

-t-) 

CO 

C 

_o 

*w 

•w 

I § 

i 3 

■3-'2 

E ,o 


3 

w 


C/D 

CL 2^ 

o ^ 

L 

CL .5 
3 C 

^2 

S 

CL O 

< E 


C 3 
^ g -O 
CO CS 
00 

_ 3 

Of) CO 

3 


3 

C 

o 


CO 


■sg i 

oE: o -o 



o 

o 

w 

OX) 

_c 

’E 

3 

2 

3 

00 



li 

Q 


c 

3 

E 

CO 

C/D 

0) 

C/D 

C/D 

< 

-o 

c 

3 

Of) 

c 

'C 

o 

”2 

o 


3 

O 


u. 

,o 


^ •- 
O 

0|-< 

O 

s 

Oh 


Of) 

S O 
o c 

&•- 
o 

3 

c 


"O 

G 

3 


o 

Of) 

G 

CO "g 

3 .5 


L»!i 

L 

o 

3 


G 
3 

E ‘- 

3 


3 


s|l 

(Tl 


00 

o 

_ I 

3 o 

CO >H 


G 

i 


C ^ 
w 3 
3 L 
3 


T3 

G 

3 


00 

O 

£§>■■§ 

ci o 

0(-i TJ L 


CO 

Of) 

G 

■■E 

o 

CL 

CL 

G 


CO 

o 

CO 

3 


CO 

3 

CO 

00 

3 

3 

O 

L 

CL 

Of) 

.E 

•4—1 

CO 


C3 CO !Z) LC 


•4—> 

G 

3 



CO 

3 

3 

•4-4 

G 

3 

3 

CO 

o 

3 

CL 



O 

w 

o 

L 

CL 

3 

o 

CO 

OU-I 

O 

1 

CN 

O 

E 

L-h 


j_; 

0(_l 

O 

•4-1 

Of) 

t: 

o 

CL 

00 

CO 

O 

G 


O 

cu 

G 

3 

g 

_g 

3 

CL 

G 

CO 

3 

3 

O 

3 

"•4—< 

c 

< 

CL 

_G 

G 

L 

CL 

3 

o 

OUH 



• ^ 

Of) 

^g 

'■4—1 

CO 

Id 

> 

w 

• 

C/) 

• 

3 

3 

T3 

O 

1 

CU 

3 

> 

3 

Q 

> 

3 

L 

<2 

C/D 

3 

w 

cd 

w 

C/D 


174 











c/3 

Vh 

O 
(/) 
c/3 

— ^ 

O c/3 

‘C bJO 

*5. .S 

S c 

<2 o 

-t—> 

_e 

Cl -_ 

^ g (U 

5 2 'C 
Cl< 3 
o- C 

cd w 


tyj 


O 

Oh 


<U 


d ex) X o 

to ^ c: o 


O 

t/5 "O 
00 (U 
P -Q 


<u 

oo 

a 

JO 


cd 

a 


D- 

< 


2 '-^ 
a ^ 
^ £ 


o ^ 

:d 

X) 


</5 O 
CJ 


C- = 

(/D O 


:d 

^ ,o "O 

•B 

9- Z ^ 

c/^ o 

o2 CQ C 


X 

o 


o c 


00 .3 
- "O 
0) 
00 


O “O 

'n 0) 

3 "O 

.- <U 

- X 


O 
0/2 
o 

oB '=X) X5 


(D 


S £ 


< 

rs 









fcJO 

s 


c 



1 

C/3 

C/3 

2 

•3 

3 

3 

A \ 

X 

•c 


_3 

Olh 



O 



o 

<u 



’■HJ 

o 

r“ 

13 

(j 


u 


3 

3 

4—1 

CL 

bt) 

.£ 

3 

3) 

3 

• 

C/3 

Cd 

B 

c/3 

Ui 

O 

c/3 

C/3 

<u 

3 

(U 

"3 

t , 

o 

Q 


C/D 

(/3 


<L) 

O 

3 

3 

-C 

w 

o 

U 

o 

c/3 

w 

C/D 

<2 



Cd 


o 

^3 

x> 

C/3 

M 

CU 

CJ 

o 

r- 


u 


U 


c/3 

D 

2 

w 

!& 

<D 

u 

•4-» 

2 

■g 

\o 

ad 

<N 

(D 

< 

Oh 

o 

D 

C/) 

c/3 

c/3 

*w 

3 

r*^ 

1 

P 

o 

'£ 

O 

JJ 


s 


OJ 

£ 


• f«H 

W 

£ 

X 

*c3 


o 

CL 

-C 

CL 

3 

13 

3 

• ^ 

(D 

00 

r^i 

< 

o 

(/3 

< 

.Lh 

Of-l 

CJ 

c/3 

£ 


Q 


Lh 

(/3 

T3 

O 

X 

D 

6 

w c 

c O 

^ <D 

^ c 

13 "3 

^ O 


oo 

-4-H 

O 

cd 

O- 


<U 

> 

w 

3 

£ 

3 

o 

W) 

3 

"c« 

i/j 

(U 

1/3 

Cfl 

cd 


(U 

oo 


5 tx) cb 


W) 


X 

2 

■a 


_3 

C/3 

X 

<u 

3 

o 

Cd 


bX) 

3 

_3 

is 

1 

o 

3 

03 

c/3 

>» 

C/3 


_ d3 

= “I 

O 00 ^ 
CL ^ lU 

9- a = 

0/3 P O 

C S. B- 
O o p 
5_ 3 " ~ 

^300 

a - c 00 

> o <^X) ^ 

flj '— “ - 


00 


O 


Q X 



0/3 

Cu Cd 


1/3 

(V) 00 oo 

3 T-1 
Cd ^ OO 

o C 00 

oo cd cd 


00 

(U 


cd 

<u 

o 

5 

3 

u 

<u 

> 

O 


CO 

e 

a> 

*-> 00 
oo 1_ 

O 

oo CO 
- CO 

-L- <u 


cd 


D 

CL 


CO 


L/ 

.5 P -rt 

M3 I ^ 


c is c 
c ^ M 
XG O 

Q £ b 


Cd 

2 

CL 

CL 

cd 


cd 

H-> 

Cd 

TS 

Ohh 

o 


3 

'O 

c 

cd 

3 

3 

O' 


CO 

HH 

o 

3 

.1 

D 

*■(-1 

jd 

3 

£ 

3 

u 


(/) 

a 

<l) 

W 

C/3 

P 

cd 

w 

£ CO 

0) <u 
£ .O 


c/3 

£ 

D 

w 

CO 

CO 

W) 

.£ 

’£ 

(U 

2 

o 

C/D 


w 

> 

•c 

0) 

Q 


oo 

Ul 

o 

CO 

CO 

2 


3 

•w 

3 

D 

-t-i 

o 

CL 





175 














o 


c 

o 

<u 

o 

72 (D 

—• u. 

CJ[) 

.21 


o 

’vl 

O 


o 

oo 

O 


on 

4—1 

u 

cd 

a 

e 

cm 

c 

S 

_c 

oo 

S ia 

00 


W) 

4—1 

cd 

Vm • • 
W (J 

C/5 ^ 


c/5 

00 

O 

U( 


r3 

O 


c/5 

C/5 

r3 

— <N 

CJ ^ 


c/5 

r" 

o 

•— (U 

CD — 

u 

o ^ 


:d 

a 

oo 

Id 

'3 

cd 


E 

S 


CQ O 

— $ cb 5 ^ 


:s ^ 

< <i= 


C3 


E O ^ W 


"5- '■— 

_o ^ 

(U o 

> 'c: 

(O 

■a 


ci) .y 
o 


<D 

U 


CJ 

O 00 

O CO 

00 CJ 

i'i 


o 

oo 

CJ 

.w 


r~ r3 r— 

3 n ^ 

7D O c 

'= “ 
p ,C 


C3 

CJ 


<U dj 

o 

C CJ 
Cd ■“ 

.■90 = 

= ii aj 
"cj 


(U 

CJ 

id 

-a 


O 

U 

(N 


CO 

-o 


aj 


ZZ <N 

2 3 

= -5 

aj 3 


a 


O 

< -a 


CJ 

00 

3 

o 


'o 

c 

o 

<u 

CJ 

c 

cd 

.'H CO 

^ i 

2 I 

Cu 

^ 00 


aj 

CO 

O. CO 
CO C- 

aj .- 

o_ 

iL 

S o 

00 •” 
aJ ::3 


fy) 


O 


aj 

T3 

o 

£ 

Id 

3 

4 —> 
Oh 

<0 

CJ 

C 

o 

U 


aj 

£ 

aj 

jC 

o 

CO 

C 

o 

• ^ 
•w 
cd 
O 

*c2 

c/5 

Cd 

u 


3 

CXJ 5 

O. o 
O CJ 

aj cz: 
> 


aj 

-o 


u 
aj 00 


r> 

^ >% 
00 ~ 
~0 3 

O 3 


CO 

o 

X 

O 


I'i 

C/5 

(D Q 
C/5 ^ 

C/5 c/5 

Cd 

. o 
£ 

aj 

CJ 


CO 

■o 

c 




aJ fcu 

•S P 


aj - 


aj 

r- 

CJ 



O ^ 

CJ O o 

aj o. CO 



<0 

C/5 

c 

0 


p 

X 

CO 

3 

.2 



t-i 

P 

4—1 

' 4 —* 

3 

CO 

.2 

4—> 

cx 

c/5 

p 


3 

3 

"x 

_s 

Ux 


£ 

£ 

"cd 

C 

}L 

0 

c/5 

>% 

X 

3 

p 

4 —> 
CO 

0 

p 

P 

P 

3 

C/5 

£ 

CO 

Oh 

CO 

0 

CO 

CO 

3 
—< 


C/5 

OX) 

.s 

X 

CO 

3 

p 

p 

:o 

P 

"£ 


0 

cd 

3 

U4 

P 

> 

c/5 

C/5 

• ^ 

cd 

4 —« 

"x 

cd 

X 

0 

'5b 

0 

s 

£ 

p 

3 


c 

.2 

*4^ 

Cd 

N 

• 

W 

•C 

o 

• ^ 
Vx 

o. 

u 

a 

cd 

•c 

p 

4-H 

•C 

u 


.£ 

'C 

p 

4—1 

’C 

p 
p 

c 

’p 
p 
04 

CO 

*- 

c 

— p 

2 'C 

O w 

O 3 
3 

l-H 

c2 


T3 

c 

3 

T3 
P 
73 
C 

P 4 
Oh C" 

3 C 
^ 2 
£ 


3 

4.^ 
• ^ 
4 O 

3 

JZ 


CO 


cm 

p 

D:i 


TJ 

p 

00 

T3 

P 

"O 

-3 


C 

cm P 

.2 £ ^ 

^ *-5 p 

P 3 C4_ 

2 ^ 

C C3 C 

3 .0 

o' — 

.2 S p ^ 

■3 -3 "O ^ 
S £ '00 p 
P P 3 c2 
ii J 3 o ct! 

cd p p p 


C/5 

a. 

<D 

•w 

C/) 

c 

.2 

*w 

cd 

I o 

i 3 

a."’ 

E .0 


>> 

P 
Cd 
3 
O' 

P 
TJ 
Cd "2 


00 

04 


p P p CO 
3 IS CO c 

II g .2 

g g S'3 
U § E E 


P 

£ 
p 
j= 
p 
00 

3 

o 

3 . & 

N 3 
’•3 3 

■§ ^ 

£ 


C/5 

J 

Q 

H 

p 


3 

_o 

’ 4 —» 

cd 

P 

t3 


p 


t-H 

.0 


CO 3 
c£ P 
^ 33 

u ^ 



CM 

o 

< 


3 

*<w 

£ 

o 

4-1 

00 

P 

3 

T3 

P 

x: 

00 

3 

p 

cd 

A 



3 CO 

55 b P 

o O 00 

3 ‘^-1 CO 

tm CO cd 

.2 Ij p 
•3 TS 3 

ia § •- 

T3 S 3 

> ^ B 
2 g 

£ -3 

00 *-* 
"3 '^-1 

o o 
0-5 4= 
<^2 2 
<^ £ p 


X) 

3 

X 

CO 

3 

O 

00 

CO 

£ 

4-4 

CO 

_P 
’ 4 —> 

a 

3 

O' 

3 


-3 

P 

^ a 

4 —> "3 

3 P 
P X 

£ ”3 

=* £ 

3 ^ 

3 aj 

O -3 
w 3 
3 P 

P So 
-3 3 

3 CO 


P 

X 


O 

2 S 

-3 X 

CO ^ 

4-> — 

3 cd 

p P 

.§ 1 
-3 P 
p X 

CO p 



176 














00 

<U 


t: 

(U 

o 

c 


c3 

Ui 

(U 

> 




2 «= 
2 'i 


CO 

T3 

O 

Si 

D 


>> 

C 


<u 
o 

c _ 

C3 D 
*1^ 


u 

cd 

> 


c/3 

13 

T3 

O 


c/3 

D 

JH 

O 

cd 

O 

Ui 

Cl. 

Cl. 

cd 

Id 

■t—> 

c 

<u 

6 

'C 

<u 

a. 

X 

W 


<u 

^ X 
o <u 

<=> c 
*-* 5 

00 .2 
'rt •.-> 

2 

43 B 

^ .S 


o o 


c/3 


(/) 

^ a 
•« 2 
cd t) 

e 

(L> C 

- ii o 

00 cd 

tJ -2 

o 

<u ex g 

5 3 0 

00 >_( 


o 

T3 

o 

B 

T3 

C 

cd 


73 

O 

-C 


c 

o 

a 

<D 

W) 

cd 

§ 

a 

(j 

52 

'o 

<u 

ex 

00 

O 

> 

O 00 

*2 "3 

o o 

cd W) 


u W) 

c 2 .a 

fe 

O CJ 

^ a 

I « 

^-1 00 

^ T? 

■S ^ 

c/3 
kH 

O 


(Zi 

c 

.2 c« 

2 

.-(DO) 

a 

• ^ a_> 


C/3 

U 

a 

.o 


o 

73 

O 


cd 


a ^ 

o ^ 

H-h 

13 

cd D 

• fl) -taJ 

"5 R 2 

t-i CLi o 

<U t7 H 

13 O *,3 

cd w ^ 

^ .a .a 


o 

W 

2 

o 
73 
O 


73 

e: 

cd 


O 


C 

2 3 
cd 

ex 

cd 
o 


O 

c/3 

a 


s ^ .2 


.'33 73 
X) o 
J2 td 

•g .-a 

^ 13 

< > 


2 ^ 

a a 
o 2 - 

is 

ex X 

Cd o 


oo 

<U 

-S 

=3 


O 

c/3 

Cd 

U 


00 

O 

X 

a 

ex 

p. 

cd 


I 

O 

U 


00 

ex 

o 

-4—> 

00 

C 

O 

•w 

cd 

w 

c ^ 
2 o 
S td 

fl) -taJ 

B .o 


o 

o 7 : 

c/3 ex 

3 X 
cd X 

o E ^ 
cd GJO O ro 

O C c/3 ^ 

O O ^ O 

== s a cx 

< i to < 


tuo 

C 

o 


00 
O 

c/3 

O cd 
X O 
G c/3 Oh 


O 

O 


c 

o 


00 


•n o 


cd u 
3 cd 
'-' 

cd o 


£- CO 
3J O 
3 CX 

w .a a < 


Vx 

O 

too (/3 

C D O 
X > X 

00 CJ 

cd R cd ^ 
CJ .2 O 31 
73 O 
^ (D ex Q_ 
o £x cx 5 

lx Ph cd <4 


hJ 

Q 

i-= 

A o 

W 

C/3 

^ * 1 ^ 
Q ^ u- 

O 'Td 

^ ^ 

Cu A X 

<+x II ^ 
^ 13 

fcJO CO 5 

cd O > 

C A 


^ TO 
C/3 73 

O 


.2 
’*—> 

c/3 

O 


Cd 


o 

."a 

> 

o 

Ui 

cx 


=2 

"a 

W) 

c 

0 

B 

73 

c/3 

0 

w 

cd 

c 

cd 

(U 


(U 


"p 

c/3 

c/3 

C/3 

0 

C 

0 

:3 

cd 

0 

c 

C/3 

cj 

C/3 

"O 

C/3 

<D 

C+-I 

C/3 

<u 

c 

w 

0 

Ul 

w 

cd 

c/3 

c 

c/3 

C/3 

4 ) 

0 

fli 

"O 




0 

X 

*w 

cd 

*• 4 — > 

W 

(U 

3 


3 

a 

a 

3 

a 


.0 


o 

X 


cd 


c/3 

U 

O 

00 

c/3 

o 


fcJO 

c, 

O' 

a 

cd 

00 

C 

**-> 

o 

cd 

Ui 

o 


00 

o 

I 

o 

X 


c/3 

c a 

^ 2 

.2 o 

Xl £. 

ex o 
o cj 

13 .a 

I" 

X 
fO ^ 

O -2 

Oh O 

< 2 


<D 

> ^ 
2 o 

■-S 

Ph? 

^ X 

O W 
00 ^ 

.a X 

^ o 

x> 2 

a g- 

o ex, 

X cd 


177 















1/5 

*■(—> 

’c 3 

t: 

<u 

o 

c 

D 


cd 

Ui 

D 

> 


c/o 
CU 
D 
-<—• 
c /5 

C 

*■(—> 
cd 

w 

5 ? t /5 

o aj 

S ^ 

(H W 

’^-'2 

E ,o 


Q 

i-- 


c /5 ^3 (/^ 

^ 2 - 

P S "O 


u m 


n A < 1 ^ 

A x: 


c /5 

O S' 

OX) m 5 
cd O ^ 

e 


A 


o OX) 
o c 
(N "r! 

-C 

< OX) 
P- t: 


m 


"8 
<u 


>> 

w 

'E 

t: 

(U 

u 

c 

=s 

<u 

> 


53 o 

<u 

= o 
r 3 c: 

O "rt 

2 '3 

c/) O 


E JS 

'-o 3 

c S 

cd 3 
O «-> 

S c« 
c<o 

qj <U 
</5 

CJ </) 
c« Cd 


>> 
-4—* 

c 
"3 
t 


<u 

^ S ^ 

OX) B 

C 5/3 

'S 73 

x: o ■ . 

OX) x: 2 

• wm ^ C 

< 1 ) (D C 

^23 


C 

3 

<U 

"■•-> 

cd 


c /5 

> 

c« 55 

<D x: 
i 3 OX) 

.3 ® 
. 1-1 

o ^ 

8 * 
V- 


« tJO 

D-.E ^ 

OX) C 
C Q 


>5 

4—1 

3 

3 

t: 

(U 

o 

c 

3 


3 

O 


^ .3 

S t: 

Dii Oh 


cd 
o 
w IS 

04 W 

*,^3 ^ 
■3 

3 =3 

E cy 


(U 

o 

3 

(U 

3 

> 

D 

< 4 i< 

O 

I 

4 —> 

.£? o 

D O 


3 

O 

• 1M4 

w 

cd 

o 

c 

• 

W 

3 

3 

3 

cr 

3 

o 


"04 

E 


<u 

c /5 

3 

3 

O 


(U - 
O c /5 OX) 

3 3 3 c /5 

3 0-33 

3 X 2 is 

• in 4 -> -3 3 

3^22 

O E H E 



c /5 

(_ aj 

,9. '3 

4-1 O 


■8 

X 

w 

<u 

E 

o 

> 


E .2 


X 
'04 

E 
o 
o 

OX) 

3 .E 

• f«H W 

3 

i .-E 
col 


73 

4-1 

O 


•5^ -r 

to 3 

3 

3 — 

OX) 3 

3 W 5 Q 

^'Bx) 

s s 

^ ’o 

*5 <u 2 

8^3 

Q C/5 Uh 

- -3 i-ia 

S 2 S f 3 

E —' E E 8 


c /5 


Ui 

c/) 

<U 

3 

.S" 

‘S 

X 

o 

5J c/5 

"oS 
•- "S 

1/5 O 

3 X 
3 S 
& 

H S 
c o 
W o 


D 

73 

O 

E 


3 

.2 

’ 4 —> 

3 

."2 

*3 

> 


C 4 _ ^ 

o 2 

^ (U 

.2 — ^ - 

"o O c^ 

S" I ■? O 


c /5 

o -i: 

o 


_ JZ 
<U 0 ) 


<u 

CJ 

3 

•o 


C/D *w 

00 1 : 

o 

3 O 


5 o 

a 3 u 

O (U 3 3 


OX) 

s 

4—1 

3 

3 

O 

o 

o 

3 

"3 

4-1 

3 

I 

3 00 

I’S 
•g -s 

3 

W E 



3 

o 


3 c/) 



OXj 

■•Z "O 

o 

” 8 

53 

Cd 
O 1 - 


o 

r- 

3 


0 ) 

O 


5 


“ C ^ 

-g O 

3 to 

a 8 


4 . < 5 J 0 

^ s 

^ 

"5 2 

3 c 

O 5 P 
4 | 3 _ 

2 ^■’3 

4 ^ 3 </5 

3 ^ TJ 4 -> 

< 1 ^ "IS 

3 .1.- 

> O4 


3 

O 

4 i 'w 

O 3 
4h 4. 

r- P 

3 4-1 
OX) ^ 

.-4 <U 

c/5 4i 

i^ 4-1 
73 o 


OX) 

3 

•c 

o 

4—1 

. —4 

3 

P 


3 
0 ) 

E ^ 

c /5 ^ 

<U 
00 
c /5 


4 ) 
O 

_ 3 

3 00 


c /5 

^ P 
8 § 3 

•— a, 
4 - B- 
e < 1 ^ ’S 
S O 4 5 

rS ^ S 

U <u 3 




c /5 

O 4 

3 


00 
O 

'C 

4 —> 

<u 

u 

04 ^ 

Ui 

o 

c /5 
C /5 

E 


00 


w 

z 


o 

.44 


3 

O 4 

3 


3 

3 

3 " 

< 


.3 ^ 


.E -c 

I! 

O 4 4 ) 
3 
34 

3 ^ 
.3 o 

—> O4 
4 —> 

<U 3 
CD -3 


— M f'l 


C £ 

CD 

T3 

P ^ 

CD 

^ 3 
•w C 

73 

CD 

d) 0 

ID 

^ ’-S 

3 

c 2 

/4^ 

0 .E 

< 

•3 -p 

Cd 

< 

3 C) 

0 

^ 8 


^ - 



d) 

0 <>5 

0 

3 

* 

> 

00 0 ) 

E 3 

c 

ID 

Z -S 
p v. 

00 

CD 

> ID 


3 ^ 

.—4 

-Q 


D CD 

r3 

44 

> 


3 4 > 

2 8 


73 

3 

3 


c/5 

CD 

^ 73 

I ^ 

5 3 
3 ID 

73 O 4 

c/5 

c^ E 

O 4 ^ 

3 •' 

4/ 4-1 

U 4 2 

bX) 

'C 

4 -> 

3 
3 


O 

CO 

4 i 

o 

c /5 


OX) 

ID 


c/5 
D 1/5 
3 4 < 

r/? P 
C/3 c/5 

f 5 C^ 

-- E 

2 c/5 
^ 2 

< ^ 

3 2 
d) I 

jz a 
o ^ 


04 

3 

P 

t .4 

OX) 

4 i 

o 


3 

I 

• ^ 

73 

ID 
00 

73 

ID 

73 
73 

ID 

CQ 
73 

3 
3 

73 
ID 
73 
3 
CD 
O 4 
c /5 

3 

00 fa 

4 . ID 
00 43 

3 U 

ID 


O 4 

3 

O 

4< 

OX) </5 

p 

^ p 


73 

3 

3 


3 

CD 

s 

(/) 

c/5 

CD 

00 

00 

< 

CD 

OX) 

3 

E 

3 

Q 


CD 

E 

3 
o 

>4 l/i 
O 4 <D 
3 — OCi 

§82 

E ■ 


00 


3 

o 


.Sh cj 


3 

iz; 

OX) 



3 

P 

44 

OX) 

4 l 

P 

00 

00 

ID 


E 

p 

, 4 ( 

4 < 

< 4-1 

P 

5 > 


Vx 

W 

13 

z 

'x 

p 

H 


3 

■p 

73 


.X>S 



< 4 - 

c /5 


c /5 

</5 

0 

c /5 

00 

"3 

0 

44 

p 

4 i 

4 -< 

00 

0 


3 

4 —> 

3 

73 

3 

X 

3 

4-4 

3 

p 

4-4 

3 

CD 

U 

0 

C /5 

C /5 

d) 

0 - 

P 

OO 

c /5 

ID 

44 

3 

• ^X 

73 

CI 4 

3 

P 

a 

c /5 

U 

0 

4 —» 

3 

3 


CD 

00 

3 

73 

CD 

> 

^3 

Vx 

- 4 —» 
00 

4 i 

4—1 

00 

iD 

4 —* 

2 

OX) 

X 

4—1 

3 

C /5 

C /5 

d) 

cr 

< 


P 

O 4 

00 

"O 

3 

4 .^ 

3 

0 

cd 

u 

• XX 
W 

cd 

p 

& 


3 

3 

E 

w 

00 

E 

cfa 

O 4 

3 

0 

E 

1 

Un 

0 

1 

3 

.2 

" 4—1 

73 

CD 

3 

3 

cr 

< 

3 

cr 

< 

p 

0 

^3 

3 

2 

*w 

Cd 

p 

73 

E 

-w 

Cd 

3 

cr 

W 

=5 

a. 

u 

a 

C /5 

C /5 

E 

3 

3 

u 

00 

P 

O 4 

E 

p 

E 

p 

<D 

X) 

/-s 

bx 

d) 

w 

cu 

< 

c 

HH 

HH 

00 

w 

00 

■E 

X 

ID 

U 

4 i 

Uh 

H 

C 


~ o "" 

00 o\ — — 


178 











Appendix 2. Opportunities for interlaboratory collaboration. 



179 

























'I 



I 





I- It 


t 








s>EPA 

United States 
Environmental Protection 
Agency 


National Health and Environmental 
Effects Research Laboratory 
Research Triangle Park, NC 27711 

Official Business 
Penalty for Private Use 
$300 

EPA/600/R-02/074 
September 2002 


Please make all necessi 
detach or copy, and retu 
left-hand corner. 

If you do not wish to rec 
detach, or copy this cov 
upper left-hand corner. 


LIBRARY OF CONGRESS 



0 010 551 862 6 














































• 





>P'^j 



















( 
































































